International Journal of Bio-Inspired Computation (45 papers in press)
Magnetotactic bacteria optimization algorithm with self-regulation interaction energy
by Jiao Zhao, Hongwei Mo
Abstract: Magnetotactic bacteria optimization algorithm (MBOA) is a new optimization algorithm inspired by the biology characteristics of magnetotactic bacteria in the nature. The original MBOA is vulnerable to premature convergence and has poor exploration capability. In this paper, a modified magnetotactic bacteria optimization algorithm (MMBOA) is proposed to improve the performance of algorithm. It uses a kind of self-regulation interaction energy to enhance the diversity of the swarm for encouraging broader exploration. And as the number of iterations increases, the self-regulation interaction energy can keep MMBOA convergence. Convergence analysis of MMBOA is also implemented. The proposed algorithm converges to the global optimum with a probability one when the number of iterations tends to infinity. Experimental results on CEC2013 and a part of CEC2011 benchmark functions show that the proposed algorithm is efficient and robust, especially in optimizing multimodal functions.
Keywords: Magnetotactic bacteria optimization algorithm; self-regulation interaction energy; exploration; global convergence.
Formation control of multiple UAVs via pigeon swarm optimization
by B.A.I. TINGTING, WANG DAOBO, Zain Anwar Ali, Suhaib Masroor
Abstract: A distributed coordinated control is proposed for the group of UAVs to map the behaviour rule for the pigeon group. Moreover, on the basis of pigeon inspired optimization (PIO), the UAV groups are clustered and synchronized through the coordinated control algorithm. To control the speed, direction of nth UAVs for the desired formation of flight are controlled by proportional integral (PI) and proportional integral differential (PID) controller. A high degree of convergence control method is presented to control the UAV groups at the same level of height during formation of flight. On the basis of pigeon flight obstacle avoidance, a method of UAV obstacle avoidance is designed, and a method of obstacle obstructing is also designed along with the improved artificial physical method. The proposed methodology provides obstacle avoidance for a group of UAV and overcome traditional physical methods, and can easily make the desired formation in which sudden obstacles lie in the way of formation through the arrow channel.
Keywords: Pigeon Swarm Optimization; Unmanned Aerial Vehicle; Pigeon Behavior and Coordinated Control.
Research on the ensemble feature selection algorithm based on multimodal optimization techniques
by Yan-li Wang, Bo-yang Qu, Jing Liang, Yi Hu, Yun-peng Wei
Abstract: Feature selection is essentially a high-dimensional combinatorial optimization problem. To find representative feature subsets, the selection method needs powerful exploration ability. In addition, if alternative feature subsets could be provided, the final prediction accuracy can be improved by ensembling these subsets. Multimodal optimization (MO) methods with high exploration power and can find multiple suitable solutions in one single run. Therefore, this paper presents an ensemble feature selection algorithm based on multimodal optimization techniques. Differential evolution based on fitness Euclidean-distance ratio (FERDE) algorithm is utilized to search for multiple diverse feature subsets in the huge feature space. A set of diverse base classifiers are built based on these subsets and ensemble to improve the final classification performance. Compared with several existing classical algorithms and ensemble feature selection methods, the proposed method can achieve higher predictive accuracy.
Keywords: feature selection; ensemble learning; multimodal optimization; classification.
A Novel Oversampling Technique Based on the Manifold Distance for Class Imbalance Learning
by Yinan Guo, Botao Jiao, Lingkai Yang, Jian Cheng, Shengxiang Yang, Fengzhen Tang
Abstract: Oversampling is a popular problem-solver for class imbalance learning by generating more minority samples to balance the dataset size of different classes. However, resampling in original space is ineffective for the imbalance datasets with class overlapping or small disjunction. Based on this, a novel oversampling technique based on manifold distance is proposed, in which a new minority sample is produced in terms of the distances among neighbors in manifold space, rather than the Euclidean distance among them. After mapping the original data to its manifold structure, the overlapped majority and minority samples will lie in areas easily being partitioned. In addition, the new samples are generated based on the neighbors locating nearby in manifold space, avoiding the adverse effect of the disjoint minority classes. Following that, an adaptive adjustment method is presented to determine the number of the newly generated minority samples according to the distribution density of the matched-pair data. The experimental results on 48 imbalanced datasets indicate that the proposed oversampling technique has the better classification accuracy.
Keywords: (class imbalance learning; oversampling; manifold learning; overlapping; small disjuncts).
A real adjacency matrix-coded evolution algorithm for highly linkage-based routing problems.
by Hang Wei, Han Huang, Zhi-Feng Hao, Witold Pedrycz, Qin-Qun Chen, Gang Li
Abstract: In routing problems, the contribution of a variable to ?tness often depends on the states of other variables. This phenomenon is referred to as linkage. High linkage level typically makes a routing problem more challenging for an evolutionary algorithm (EA). An entire linkage measure, named entire linkage index (ELI), has been proposed in this paper for such routing problems. Aiming at solving high linkage-based routing problems, we presented a real adjacency matrix-coded evolution algorithm (RAMEA) that is capable of learning and evolving correlation matrix of decision variables. The e?ciency of RAMEA was tested on two familiar routing problems: traveling salesman problem (TSP) and generalized traveling salesman problem (GTSP). The experimental results show that the RAMEA is promising for those highly linkage-based routing problems, especially for those of large scale.
Keywords: linkage; routing problems; evolutionary optimization; real adjacency matrix-coding mechanism.
A New Hybrid System Combining Active Learning and Particle Swarm Optimization for Medical Data Classification
by Nawel Zemmal, Nabiha Azizi, Mokhtar Sellami, Soraya Cheriguene, Amel Ziani
Abstract: With the increase of unlabeled data in medical datasets, the labelling process becomes a more costly task. Therefore, Active Learning provides a framework to reduce the amount the manual labor process by querying an expert for just the labels of particular instances, the choice of these instances to annotate is paramount. However, the traditional active learning techniques can be computationally expensive as they require to analyze, at each iteration, all unlabeled instances including those that are redundant and uninformative, thereby decreasing the system performance. To handle this issue, it is necessary to have a global optimization algorithm that allows finding the best solution in a reasonable time. This paper proposes a novel framework combining active learning and particle swarm optimization algorithm. A novel uncertainty-based strategy was designed and integrated into the PSO as an objective function. This new strategy allows finding the most informative instances by calculating an uncertainty score using instance weighting method. Experiments were performed on binary and multi-class classification problems using both balanced and unbalanced medical datasets. Experimental results show that the proposed uncertainty strategy outperforms its existing counterparts. It achieves performances comparable to supervised methods.
Keywords: Active Learning; Uncertainty Sampling Strategy; Particle Swarm Optimization; Global Optimization Problem; Instance Weighting.
DVR Based Power Quality Enhancement Using Adaptive Particle Swarm Optimization (APSO) Technique
by Ravi Srinivas Lanka, Mahesh Babu Basam, Tulasi Ram S.S.
Abstract: This paper proposes a heuristic control of the series active power filter for power quality enhancement. In the context of power quality, the series active filter is better utilized as a voltage source controller contrary to its conventional usage as variable impedance. The present-day utility system as a linear model is unsatisfactory and the steps are laid down to discuss utility systems as a nonlinear model. This paper deals with comparative analysis of particle swarm optimization (PSO) and its contemporary modified PSO algorithms modified PSO (MPSO), Adaptive PSO (APSO). The harmonic reduction in the source current and sags/swells mitigation in the load voltage is carried out with the optimal tuning of the PI controller. The Series active power filter as a harmonic suppressor with a specific reference controlled strategy is discussed in this paper. The synchronous reference frame (SRF) Theory and its modified versions are used to generate a compensating signal. The hysteresis band current controller (HBCC) is used to perform the switching operation of Voltage Source Inverter. Simulations are carried out in the MATLAB/SIMULINK environment.
Keywords: Dynamic Voltage Restorer (DVR); Synchronous Reference Frame theory (SRF); Particle Swarm Optimization (PSO); Modified PSO (MPSO); Adaptive PSO (APSO).
Pigeon-inspired optimization algorithm with hierarchical topology and receding horizon control for multi-UAV formation
by Yankai Shen, Yiming Deng
Abstract: Multi-UAV formation is a crucial research aspect of UAV cooperation control. This paper proposes a hierarchical topology pigeon-inspired optimization (HPIO) and a receding horizon control (RHC) to deal with the problem. The RHC method can convert the problem into an online optimization problem. Besides, inspired by the structure of pigeon flock, a new topology with a hierarchical structure of pigeons is designed. The velocity updating formula of the original PIO is redesigned. Then discuss the stability of the modified PIO. Finally, use the HPIO to generate the desired formation control in each time domain of the RHC. The simulation results verify the feasibility and effectiveness of the proposed method.
Keywords: Multi-UAV Formation; Receding horizon control (RHC); Pigeon-inspired optimization (PIO); Hierarchical topology.
Top-Down modulated model for object recognition in different categorization levels
by Fatemeh Sharifizadeh, Mohammad Ganjtabesh, Abbas Nowzari-Dalini
Abstract: The human visual system contains a hierarchical sequence of modules that take part in visual perception at superordinate, basic, and subordinate categorization levels. The top-down signals facilitate the bottom-up processing of visual information in the cortical analysis of object recognition. We propose a novel computational model for object recognition in different categorization levels, which mimics the effects of top- down signals in the hierarchical processing of the visual system. The top-down signal is incorporated in bottom-up processing of input image to increase the biological plausibility of our model as well as its efficiency for the object recognition in different categorization levels. The top-down signals provide a pre-knowledge about the input space, which can help to solve the complex object recognition tasks. The performance of our model is evaluated by various appraisal criteria with three benchmark datasets and significant improvement in recognition accuracy of our proposed model is achieved in all experiments.
Keywords: Object Recognition; Categorization Levels; Computational Models; Bottom-Up Processing; Top-Down Signals.
Pigeon inspired optimization based cooperative target searching for multi-UAV in uncertain environment
by Delin Luo
Abstract: In this paper, the multi-UAV cooperative target searching problem is investigated and a close loop path planning method is developed for UAVs in uncertain environment. The proposed method includes two consecutive parts, the multi-UAV cooperative target search algorithm developed based on cooperative pigeon inspired optimization (CPIO) and the base returning algorithm for each UAV based on artificial potential field (APF) method. Firstly, a concerned regional environment and the initial search probability map models are established. Then, by applying the rolling prediction strategy, the cooperative target search paths for multiple UAVs are generated by utilizing the proposed CPIO. With this method, UAVs can reinforce target search in the key areas in a cooperative way and avoid flying into the no-fly zones. In the meanwhile, the Bayesian theorem is used to constantly update the search probability map in each search step. Finally, at the end of the target search phase, an optimized safe path is generated for each UAV returning back to its original by using the APF method. Simulations are performed and the results demonstrate that the proposed approach is effective for multiple UAVs carrying out cooperative target search task in a complex environment.
Keywords: multi-UAV; cooperative search; pigeon-inspired algorithm; artificial potential field.
Binary Fireworks Algorithm Application for Optimal Schedule of Electric Vehicle Reserve in Traditional and Restructured Electricity Markets
by Srikanth Reddy, Lokesh Panwar, B.K. Panigrahi, Rajesh Kumar
Abstract: The electric vehicle (EV) is a proven and reliable technology to curtail global emissions in the transportation sector. The multipurpose use of EV makes it a special entity not only in transportation sector but also in the modern smart grid. This paper presents an application of Binary Fireworks Algorithm for solving a multi-beneficiary model in which conventional thermal units are scheduled considering EV aggregator as an identical entity to GENCO in offering reserve services. The fast response of EV can be exploited in providing reserve capacity along with traditional unit commitment (UC) in regulated and price based unit commitment (PBUC) of thermal generators/units in deregulated electricity markets respectively. This paper extends the application of Binary Fireworks Algorithm (BFWA) to resource scheduling in smart grid to optimize the generation schedule of thermal and EV reserve in the day-ahead market. Also, develops first application of BFWA algorithm to solve PBUC. Thereafter, the resource scheduling objective in PBUC is extended to include EV reserve. Simulation results are presented for discussion and the same reveals that such reserve market participation of EV can improve the economics of both utility and EV. Also, compared to energy market participation, in reserve market profit for EV is increased by $3.59/MW and $3.96/MW in deregulated and traditional market scenario respectively. In addition, comparative analysis for two types of EV namely, battery electric vehicle (BEV), fuel cell electric vehicle (FCEV) is carried out with respect to cost-benefits. The market participation, especially reserve market participation proved much profitable for BEV compared to FCEV.
Keywords: Binary fireworks algorithm; Computational Intelligence; Unit Commitment; Profit Based unit Commitment (PBUC); Electric vehicle (EV); Electricity markets.
An improved extension neural network methodology for fault diagnosis of complex electromechanical system
by Yunfei Zhou, Yunxiu Sai, Li Yan
Abstract: Fault diagnosis is a complex and challenging problem in the operation of machines and equipment, the crucial component of which is the recognition, extraction and classification of fault features, which are closely related to the training sets. A good diagnostic method can yield twice the result with half the effort result. In the existing intelligent diagnostic methods, artificial neural network (ANN) is widely used to identify and classify fault patterns. Although this approach solves the Fault diagnosis problem of complex electromechanical system (CES), there are still two defects: (1) The training process converges slowly; (2) Feature recognition and extraction are largely influenced by the training data sets. In order to solve the above problem, we propose a novel intelligent fault diagnosis method based on the extension neural network (ENN) and a uniform distribution search for particle swarm optimization (UPSO). The data set of the turbine generator set verifies the validity of this method. The diagnostic results show that compared with the existing method, the proposed method can not only extract the available classical domain characteristic from the collected training data set adaptively, but also have high diagnostic accuracy and rapid training process.
Keywords: fault diagnosis; complex electromechanical system; particle swarm optimization; extension neural network.
ELM-NeuralWalk: Trust evaluation for online social networks
by Shuoshuo Zhang, Xiangrong Tong, Shuigen Wang
Abstract: Trust relationship plays an important role in online shopping, recommendation systems, Internet of Things, etc. The problem of trust evaluation among users in online social network (OSN) has attracted much attention, and has become a hot issue in the domain of social computing. However, the way of trust propagation and aggregation in OSN is still not clear, as well as the accuracy of trust calculation. In order to calculate the indirect trust, an ELM-NeuralWalk algorithm to implement trust propagation and aggregation is proposed. ELM-WalkNet firstly learns two-hop trust calculation rules, calculates two-hop trust among users in the OSN. After that, ELM-NeuralWalk updates the OSN with the calculated trust value, so as to realize the calculation of multi-hop trust among users through iterative calling ELM-WalkNet. Unlike traditional solutions that use inference methods, ELM-WalkNet can learn trust calculation rules in an inductive way and accurately calculate indirect trust between users. Experiments on two real OSN datasets showed that ELM-NeuralWalk outperforms existing solutions.
Keywords: Trust; online social networks; extreme learning machine; machine learning.
Jumping particle swarm optimization method for solving minimum weight vertex cover problem
by Balaji Sankarasubramanian, Kanagasabapathy G, Vikram S. T.
Abstract: The minimum weight vertex cover problem (MWVCP) is one of the classic combinatorial optimization problem. In this paper we have proposed a modified jumping particle swarm optimization approach, named JPS-VC, for MWVCP. This method contains two new local search approaches incorporated with the jumping particle swarm optimization approach and it admits several remarkable features such as novel construction of feasible solution and also the quick conversion of feasible solution to optimal solution. The computed results indicate that the proposed approach has got appreciable performance over the recent state-of-the-art algorithms.
Keywords: minimum weight vertex cover; discrete particle swarm optimization; computational methods; combinatorial optimization.
Effect of potential well model for quantum heuristic algorithm: A comparative study and application
by Jin Jin, Peng Wang
Abstract: The multiscale quantum harmonic-oscillator algorithm (MQHOA) is an intelligent optimization algorithm based on quantum harmonic wave functions. Although it is effective for many optimization problems, an analysis for its performance is still lacking. This paper discusses the harmonic-oscillator potential well, delta-function potential well, and infinite-square potential well in terms of their application in evolutionary algorithms. Of the three, the harmonic-oscillator potential well is considered to give the most precise approximation for complex objective functions. To verify its global optimization performance, experiments are conducted using a suite of benchmark functions to compare the performance of different potential wells and heuristic algorithms. The experimental results indicate that MQHOA with the harmonic-oscillator potential well is a better practical choice than the other two potential well models, and show that MQHOA is a potential quantum heuristic algorithm.
Keywords: Multiscale quantum harmonic oscillator algorithm; quantum heuristic algorithm; global optimization; potential well; wave function.
A novel whale optimization algorithm with filtering disturbance and non-linear step
by Jinkun Luo, Fazhi He, Haoran Li, Xian-Tao Zeng, Yaqian Liang
Abstract: As a recent addition of population-based meta-heuristic algorithms, whale optimization algorithm (WOA) has attracted a lot of attention recently. However, WOA still has room for improvement in the accuracy and reliability of solution. In this study, a novel WOA with filtering disturbance and non-linear step (FDNS-WOA) is proposed. Firstly, to enhance the population diversity and the global search ability, we design a weighted Cauchy mutation equation to perturb solution space. Secondly, the excessive disturbance may cause the imbalance between exploration and exploitation, and the quality of solutions will be decreased. Thus, before the disturbance, we propose a filtering mechanism to dynamically select different individuals for disturbance at different evolutionary stages. Thirdly, a weighted local walk mechanism is designed to improve the local exploitation capability. Different from original linear step with large fluctuation, we propose a non-linear step to reduce blind spots in the local exploitation process. Finally, the proposed FDNS-WOA is tested on benchmark functions and applied in a real world problem. The experimental results show that the proposed FDNS-WOA not only outperforms other recent meta-heuristic algorithms in term of accuracy and reliability for most benchmark functions, but also achieves satisfactory results in real world problem.
Keywords: whale optimization algorithm; Cauchy mutation; economic load dispatch; meta-heuristic algorithm; population-based algorithm; filtering disturbance; non-linear step.
An adaptive multi-objective particle swarm optimization algorithm based on fitness distance to streamline repository
by Suyu Wang, Dengcheng Ma, Ze Ren, Yuanyuan Qu, Miao Wu
Abstract: In recent years, multi-objective particle swarm optimization algorithm (MOPSO) has been paid more attention. One of its indispensable structures is the maintenance and update mechanism of the repository. The existing mechanisms are relatively simple, and most of them are based on the crowding distance sorting strategy, and not conducive to the distribution and accuracy of the algorithms. The paper innovated this mechanism and proposed an adaptive multi-objective particle swarm optimization algorithm to streamline repository based on fitness distance (FDMOPSO). Both the concept of fitness distance and the corresponding improve methods of mutation mechanism and adaptive mechanism were proposed. The algorithm itself was tested using benchmarks. The results shown that the proposed application of fitness distance had a better improvement on the convergence and distribution. Compared with other algorithms, the FDMOPSO algorithm had the best overall performance.
Keywords: MOPSO; fitness distance; streamline repository; adaptive; multi-objective optimization.
Efficient Optimization Methods in a Distributed Memory Storage System Using Data Compression
by Xiaoyang Yu, Songfu Lu, Tongyang Wang, Xinfang Zhang, Shaohua Wan
Abstract: The storage of data in a memory-centric storage system benefits from great improvements in read and write I/Ornperformance at the price of consuming a large amount of valuable memory resources. As the amount of data grows, a distributedrnsystem must scale accordingly, resulting in an increased number of nodes and greater communication overhead that may impact thernoverall performance. We believe that achieving an appropriate trade-off among computation, storage and network transportation will bernbeneficial for a distributed memory storage system, leading to higher overall efficiency. The challenges of achieving such a trade-off arerndetermining how and when this trade-off should be applied. In this paper, we explore a method to achieve this trade-off by introducingrndata compression technology in a transparent manner. Instead of focusing on specific compressed data structures, we target blockrnlevel compression for a general purpose storage system to incorporate a wide range of existing data analysis frameworks and usagernscenarios, especially with big data. A prototype is implemented and evaluated based on the memory-centric distributed storage systemrnAlluxio to provide transparent compression and decompression during write/read operations. A series of experiments for data withrndifferent types of compression ratio is conducted to determine the proper situation in which this technology can help to improve thernsystem efficiency. The experimental results prove that our approach can achieve huge write/read throughput boosts for highlyrncompressible data and performs better for moderately compressible data when there are remote operations. From the results, wernsuggest that our approach can be applied for data with a medium up to high compression ratio in practical data processing pipelinesrnand provides useful insights for optimizing future memory-based storage systems.
Keywords: System performance optimization; Data compression; Distributed memory storage.
Research on multi-UAV task decision-making based on improved MADDPG algorithm and transfer learning
by Bo Li, Shiyang Liang, Zhigang Gan, Daqing Chen, Peixin Gao
Abstract: At present, the intelligent algorithms of multi-UAV task decision-making have been suffering some major issues, such as, slow learning speed and poor generalization capability, and these issues have made it difficult to obtain expected learning results within a reasonable time and to apply a trained model in a new environment. To address these problems, an improved algorithm, namely PMADDPG, based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed in this paper. This algorithm adopts a two-layer experience pool structure in order to achieve the priority experience replay. Experiences are stored in an experience pool of the first layer, and then, experiences more conducive to training and learning are selected according to priority criteria and put into an experience pool of the second layer. Furthermore, the experiences from the experience pool of the second layer are selected for model training based on PMADDPG algorithm. In addition, a model-based environment transfer learning method is designed to improve the generalization capability of the algorithm. Comparative experiments have shown that, compared with MADDPG algorithm, proposed algorithms can scientifically improve the learning speed, task success rate and generalization capability.
Keywords: Multi-UAV task decision; Improved MADDPG algorithm; Two-layer experience pool; Transfer learning.
A new multi-objective artificial bee colony algorithm based on reference point and opposition
by Songyi Xiao, Wenjun Wang, Hui Wang, Zhikai Huang
Abstract: A new multi-objective artificial bee colony (ABC) algorithm based on reference point and opposition (called ROMOABC) is proposed in this paper. Firstly, the original framework of ABC is modified to improve the efficiency of population renewal and accelerate the convergence rate. On basis of this framework, two new strategies are proposed. In the scout bee search, opposition-based learning and elite solutions are used to reduce the waste of computing resources. Distribution of solutions is improved by using reference points associated external archive. Experiments are conducted on sixteen multi-objective benchmark functions including ZDT, DTLZ and WFG multi-objective benchmark functions. The comparison of ROMOABC with five other multi-objective algorithms shows that it has competitive convergence and diversity.
Keywords: artificial bee colony; multi-objective optimization; external archive; opposition; elite learning.
Distance-based Immune Generalised Differential Evolution Algorithm for Dynamic Multi-Objective Optimisation
by María-Guadalupe Martínez-Peñaloza, Efrén Mezura-Montes, Alicia Morales-Reyes, Hernán Aguirre
Abstract: This paper presents Distance-based Immune Generalised Differential Evolution (DIGDE), an improved algorithmic approach to tackle dynamic multi-objective optimisation problems (DMOPs). Its novelty is using the Inverted Generational Distance (IGD) as an indicator in its selection mechanism to guide the search. DIGDE is based on the Immune Generalised Differential Evolution (Immune GDE3) algorithm which combines Differential Evolution (DE) fast convergence ability and Artificial Immune Systems (AIS) principles for good diversity preservation. A thorough empirical evaluation is carried out on novel benchmark problems configured with different dynamic characteristics. DIGDE's experimental results show an overall improved statistically supported performance in terms of solutions approximation and better achieved distributions. Using IGD as a searching indicator allows DIGDE to achieve better performance and robustness in comparison to state of-the-art methods when facing different change frequencies and severity levels.
Keywords: Dynamic Multi-objective Optimisation; Selection mechanism; Inverted Generational Distance indicator; Immune response; Differential Evolution; DMOPs.
A Novel Bio-Inspired Stochastic Framework to Solve Energy Management Problem in Hybrid AC-DC Microgrids with Uncertainty
by Sattar Shojaian
Abstract: This paper develops a new management framework for optimal operation of the hybrid AC-DC microgrids incorporating renewable energy sources and storages. Hybrid microgrid consists of two parts of AC and DC to supply the AC and DC loads, respectively. The power exchange capability of hybrid microgrids between the AC and DC parts makes it possible to reduce the total microgrid costs, effectively. To make it a realistic analysis, a stochastic method based on cloud theory is proposed to model the uncertainty effects of wind turbine power, photovoltaic power, load demand and market price sufficiently. The proposed framework makes use of a new optimization algorithm based on flower pollination mechanism to minimize the total network costs through the optimal dispatch of the units. Also, a three stage modification method is proposed to improve the population diversity and avoid the premature convergence. The performance of the proposed method is examined on the IEEE test system through two different operation scenarios.
Keywords: Evolutionary Algorithm; Energy Management; Hybrid Microgrid; Flower Pollination Algorithm; Cloud Theory; Modification; Uncertainty; Optimization.
Fittest Survival: An Enhancement Mechanism for Monte Carlo Tree Search
by Jiajia Zhang, Xiaozhen Sun, Dandan Zhang, Xuan Wang, Shuhan Qi, Tao Qian
Abstract: Monte Carlo Tree Search (MCTS), which constructs a search tree of game states and evaluates expected rewards by thousands of Monte Carlo simulations, has become the pre-eminent approach for many challenging games. One severe challenge of MCTS is the contradiction between the accuracy of states' evaluation and practical time consumption for simulations, both of which are critical for a competitive game program. This paper proposes and evaluates Fittest Survival Monte Carlo Tree Search (FS-MCTS) which provides a novel mechanism to enhance MCTS towards correct direction with fewer simulations. The key idea of FS-MCTS is to keep states with significant advantages to survive while eliminate the others. This is the meaning of ``Fittest Survival''. In this sense, FS-MCTS no longer completely depends on the evaluation accuracy of game states which severely relies on adequate simulation times. We evaluate FS-MCTS in the problems of Poker. Experimental results show that FS-MCTS, combining with several variants of popular UCB policies, performs better than their vanilla versions when a certain number of simulations are guaranteed for its theoretical prerequisites.
Keywords: Monte Carlo Tree Search; Texas Poker; tree pruning; significance test.
Clustering algorithm for mixed attributes data based on Glowworm Swarm Optimization algorithm and K-prototypes algorithm
by Yaping Li, Zhiwei Ni, Weiliang Zhou
Abstract: The main purpose of this research is to improve the accuracy of clustering algorithm, which is also of great significance for big data analysis. As a typical clustering algorithm,K-prototypes algorithm directed at the analysis of mixed attributes data features simple principles and efficient operation. However, the clustering effect of K-prototypes algorithm depends on the initial clustering center, and the distance between data objects is not calculated accurately. In order to improve the clustering accuracy of mixed attributes data, Glowworm Swarm Optimization (GSO) algorithm is introduced into K-prototypes algorithm to form a new clustering algorithm in our research work. First, the idea of combining GSO algorithm and K-prototypes algorithm for the clustering of mixed attributes data is provided. Next, GSO algorithm is improved by using the good point set. Then, the improved GSO algorithm is employed to search extreme points of density in the space of data objects. The initial clustering center of K-prototypes algorithm is chosen from the extreme points of density. Meanwhile, a unified method is designed for the distance of numeric data and categorical data. On this basis, GSO algorithm and K-prototypes algorithm are combined to form a new clustering algorithm flow (GSOKP) for mixed attributes data. Finally, the UCI data sets of numeric data, categorical data and mixed data are selected to test GSOKP algorithm. And the effectiveness of GSOKP algorithm is analyzed in terms of clustering accuracy through experimental comparison.
Keywords: GSO Algorithm; K-prototypes Algorithm; Good Point Set; Clustering.
Adaptive Optimization Driven Deep Belief Networks for Lung Cancer Detection and Severity Level Classification
by Malayil Shanid, Anitha A
Abstract: Computed tomography (CT) for lung cancer detection is trending research in determining the lung cancer on its earlier stages. However, accurate Lung cancer detection with severity levels is a major challenge faced by most of the existing methods. This paper proposes a lung cancer detection model for analyzing the severity levels using the CT images. The input CT images are obtained from the input lung cancer database using which the lung cancer detection and severity level classification is performed. The shape local binary texture (SLBT) is employed, which is generated by combining Local directional pattern (LDP) and Linear Binary Pattern (LBP), which is extracted from the nodules. The features are subjected to proposed Adaptive-SEOA-DBN, which is the integration of Adaptive-Salp-Elephant Herding Optimization Algorithm (Adaptive-SEOA) in DBN for effective training of the model parameters. The proposed Adaptive-SEOA is developed by combining self-adaptive concept in the SEOA. Finally, severity level classification is done to declare the severity of patient. The effectiveness of the proposed Adaptive-SEOA-DBN is revealed based on maximal accuracy of 96.096 and minimal False Detection Rate (FDR) of 0.019, minimal False Positive Rate (FPR) of 4.999, and maximal True Positive Rate (TPR) of 96.096, respectively.
Keywords: Lung cancer; Severity level; CT images; segmentation; lung nodules.
Parameter Optimization of Sliding Window Algorithm Based on Ensemble Multi-objective Evolutionary Computation
by Guang Li, Jie Wang, Jing Liang, Caitong Yue, Taishan Lou
Abstract: The parameters of sliding window algorithm are difficult to determine. Therefore, a sliding window-based method for parameter optimization of data stream trend anomaly detection algorithm is proposed in this study. This method regards the data stream anomaly detection as a two-objective optimization problem. Three optimization algorithms and ensemble strategies were used to obtain the optimal parameter settings of the algorithm. With this strategy, it is no longer difficult to determine the parameters of the data stream trend anomaly detection algorithm based on the sliding window. Through verification of multiple real parameter data in Tarim Oilfield, it could be known that this method could realize the optimal parameter settings, which provides a reference for the parameter setting of the data stream trend anomaly detection algorithm based on sliding window.
Keywords: data stream; sliding window; parameter optimization; machine learning; anomaly detection; evolutionary computation; ensemble strategy; Multi-objective evolutionary; industry data; project parameters.
Performance Assessment of Biogeography Based Multi-objective Algorithm for frequency Assignment Problem
by Daoudi Asma, Benatchba Karima, Bessedik Malika, Hamdad Leila
Abstract: Solving Frequency Assignment Problem (FAP) with multiple objectives is NP-hard. The network has many constraints to be respected when assigning frequencies. In this paper, we consider two objectives: reduce the number of frequencies and the interference caused by the violation of the network constraints. We use a new model based on a Set-T-coloring graph which takes in consideration all constraints related to cellular network. We propose to use an evolutionary metaheuristic, Biogeography Based Multi-objective Optimization Algorithm (BBMO), to solve the considered problem. As it is a bi-objective approach, our results are represented by a set of non-dominated solutions based on the definition of Pareto optimum front. A framework to study and measure the quality of the obtained Pareto fronts based on six performance indicators (ONVGR, HV, Spread, GS, GD, IGD) has been implemented. The obtained results show the efficiency of the BBMO on FAP based on a well known COST259 data set. Moreover, a statistical analysis compares our approach BBMO-FAP to NSGA-II and identifies the best-performing method.
Keywords: Frequency Assignment Problem (FAP); Multi-objective optimization; Biogeography Based Multi-objective Optimization (BBMO); performance indicators; Kruscal-Wallis statistical test.
Open Set Recognition Through Monte Carlo Dropout-based Uncertainty
by Xiaojie Yin, Qinghua Hu, Gerald Schaefer
Abstract: Open set recognition has received much attention in recent years. In this paper, we present a novel open set recognition method that is able to obtain improved recognition by applying Monte Carlo Dropout to capture uncertainty in order to yield high quality predicted probabilities. Experimental results on six benchmark datasets show that our method gives better open set recognition performance than other state- of-the-art methods, with at least 6.4%, 3.9%, 2.9% and 1.0% performance increase in AUROC on the challenging object datasets CIFAR-10, CIFAR+10, CIFAR+50 and TinyImageNet respectively. We also perform an analysis on the benefits of combining predictive uncertainty with an EVT-based open set recognition model which indicates that Monte Carlo Dropout-based uncertainty allows to obtain high quality predicted probabilities and to learn more accurate open set recognition scores. This, in turn, helps to reduce the overlap between known and unknown classes, thus making them more separable.
Keywords: Open Set Recognition; Monte Carlo Dropout; Predictive Uncertainty.
Modeling the Green Supply Chain of Hotel Based on Front-back Stage Decoupling: Perspective of Ant Colony Labor Division
by Changbin Jiang, Ruolan Li, Jue Chen, Shufang Li, Tinggui Chen, Chonghuan Xu
Abstract: To face up to the challenges from fierce competition in traditional industry and increasing concern on environment protection, hotels in China are seeking practice of green supply chain (GSC) which helps to both raising operation efficiency and minimizing environmental impacts. Rooting from a case study in chinas hotel industry, this study intends to firstly build a new conceptual model of hotel GSC by integrating the concept of front-back stage decoupling. Then a mathematical model based on the concept of Ant Colony Labor Division is constructed to explain and verify the mechanism of the new GSC model. Moreover simulation is conducted for model testing and further discussion, which finally leading to the conclusions and future research suggestion.
Keywords: Green Supply Chain; Ant Colony Labor Division; Front-back stage decoupling.
A block-encoding method for evolving neural network architecture
by Xiaohu Shi, Hongyan Guo, Chunguo Wu, Yanchun Liang, Zhiyong Chang
Abstract: The architecture and parameters of convolutional neural networks have an important
impact on their performance. To overcome the difficulties of most existing neural architecture search (NAS) methods, including fixed network architecture and huge computing cost, this paper proposes a block-encoding based on neural architecture evolving method. A new block-encoding method is designed to divide the convolutional neural network architecture into blocks consisting of multiple functional layers. Efficient mutating operation is designed to speed up evolutionary search and expand the evolution space of network architecture. Finally, the optimal evolved network is converted into an all-convolutional neural network with fewer parameters and more concise architecture. The experiments on image datasets indicate that the proposed method can greatly reduce network parameters and searching time, achieve competitive classification accuracy and directly obtain the corresponding all-convolutional neural network architecture.
Keywords: evolving neural networks; network architecture; block-encoding; search space; all
Perfectly Convergent Particle Swarm Optimization in Multidimensional Space
by Narender Kumar Jain, Uma Nangia, Devinder Kumar
Abstract: In this paper a novel evolutionary algorithm, Perfectly Convergent Particle Swarm Optimization (PCPSO) has been proposed. This is an intelligent algorithm which does not get trapped in local minima by using personal best value along with new parameters and new velocity update equation for better exploration in the search space. The velocity clamping effectively helped to control the maximum velocity of the particles from explosion state and align them towards the true global minimum. Experimental results show that by using perfect convergent Particle Swarm Optimization (PCPSO) approach computational efficiency is increased compared to other variants of PSO and finds true global minimum in less time.
Keywords: Particle swarm optimization; Exploration; stagnation; Premature convergence; velocity clamping; PCPSO.
PSO-based Optimal Online Operation Strategy for Multiple Chillers Energy Conservation
by Jing An, Luyuan Xu, Kefan Wang, Qi Deng, Qi Kang
Abstract: As a key part of energy conservation in HVAC system, reasonable operation strategy of multiple chillers is essential to most industrial buildings. In traditional chiller control strategies, the operation state of chillers mainly depends on the experience of on-site workers. Therefore, it is important to analyse the characteristics and integrate them into a set of effective control strategy of the chiller system. In this paper, we propose an efficient control strategy for energy conservation of multiple chillers. The system energy consumption and the constrains of the chillers are firstly modelled, and a two-layer control strategy for the chillers is proposed, which is respectively used to control the selection of starting scheme of the chillers under the cooling load at the current time and the setting of control parameter values of the chiller under the selected starting scheme. The core of the two-layer strategy is the use of PSO algorithm. Experimental results have suggested that the strategy can effectively optimize the energy consumption of the multiple chillers system and realize the accurate control in different periods.
Keywords: multiple chillers; energy conservation; PSO; two-layer control structure.
A novel parameter optimization method of hydraulic turbine regulating system based on fuzzy PID controller and fuzzy differential evolution algorithm
by Zhensheng Huang, Xiaoyong Liu, Hui Fu, Zhiguo Du
Abstract: Aiming at the parameter regulation problem of hydraulic turbine regulating system with the "water hammer" effect, combining the advantages of fuzzy PID and FDE (fuzzy differential evolution algorithm), a fuzzy PID-FDE method for optimizing the parameters of the hydraulic turbine regulating system is proposed. It is used for the regulation simulation of the regulating system of the hydraulic turbine under two different disturbance conditions. The simulation results of the hydraulic turbine regulating system show that the FDE method can break through the stagnation of the DE algorithm in the later stage and continue to find a better solution. The search speed is faster than other methods but slower than fuzzy particle swarm optimization. At the same time, the simulation results also show that the parameters optimized by fuzzy PID-FDE are better, which makes the response of the simulation curve of hydraulic turbine regulating system to step signal smoother and more stable, with less overshoot and faster response speed.
Keywords: fuzzy PID; fuzzy differential evolution algorithm; hydraulic turbine regulating system; parameter optimization; Fuzzy PID-FDE.
Artificial Leaf-vein network optimization algorithm for urban transportation network design
by Baozhen Yao, Chao Chen, Wenxuan Shan, Bin Yu
Abstract: The structure of transportation networks determines the traffic flow characteristic of urban roads. In a case where demand and supply are fixed and given, the unevenly distributed traffic flow may lead to traffic congestion. Therefore, designing a rational urban transportation network (i.e., the urban transportation network design problem, UTNDP) has become one of the hottest topics in the transportation field. Due to the property of multi-objects, multi-constraints, and non-convexity, UTNDP is recognized as NP-Hard. Thus, many heuristic algorithms, based on biological laws or swarm intelligence, have been proposed in recent years. However, rare researches paid attention to the perfect topology of the leaf-vein network in nature. In this paper, a definitely new heuristic algorithm is proposed based on the leaf-vein network. By analyzing the growth process of leaf veins, the underlying relationship between urban transportation network and leaf-vein network is first investigated. An evolutionary mechanism of the algorithm or called the artificial leaf-vein generation rule, is then built by simulating the natural selection of biological evolution and genetic transmission. Given the differences between urban transportation networks and leaf-vein networks, a transformation method between the two networks is also designed. Finally, a benchmark instance, the Sioux Falls Network, is set up to demonstrate the performance of the proposed algorithm. The results show that, compared with conventional heuristic algorithms (e.g., GA, SA, and ACO), the proposed algorithm has superior performance and the high application potential in the reduction of the total travel time and length of the entire transportation network. The algorithm may help designers and planners optimize the urban transportation network and alleviate the negative effect of congestion in booming cities.
Keywords: Urban transportation network design problem; artificial leaf-vein network; heuristic algorithm; auxin transport.
An Adaptive Ant Colony Optimization for improved Lane Detection in Intelligent Automobiles Vehicles
by Salawudeen Ahmed Tijani, Sadiq Bashir Olaniyi, Muhammed Bashir Mu'azu, Ime Umoh Jarlarth, Olubukola Ishola Oyenike
Abstract: This paper presents an improved lane detection algorithm using an adaptive Ant Colony Optimization (aACO) based edge detection technique. In the paper, we first modified the ACO to select its control parameters (pheromone influencer, heuristic influencer, evaporation rate and the pheromone decay coefficient) adaptively. A threshold tolerance technique was developed to determine the element of the final ant pheromone matrix that constitutes a lane edge.Three lane detection test scenarios (Traffic, Liquid and Cloudy) was created to evaluate the performance of the develop algorithm. Simulations were performed using MATLAB and results shows a significant improvement over canny edge detector based lane lane detection method.
Keywords: Edge Detection; Lane Detection; Ant Colony Optimization; Automobile Vehicles.
Computationally Efficient Hybrid Differential Evolution with Learning for Engineering Application
by Sanjoy Debnath
Abstract: Evolutionary computation is popular as an optimization technique for its ability to achieve a globally optimal solution in a non-convex fitness landscape. Amongst the evolutionary techniques, Differential Evolution (DE) is most prominent for its relative computational simplicity and faster asymptotic convergence. However, its convergence rate is still unsuitable for real-time applications. Hence, a new leader-centric learning algorithm based on DE named Hybrid Differential Evolution with Learning (HDEL) is proposed in this work to improve the convergence performance of DE. In the proposed scheme, we introduce a novel learning-based mutation and crossover method, where the mutation and crossover strategy of the algorithm is supervised by the learning knowledge of the global best and global worst individual as well as the personal best of an individual in its current generation. The proposed method brings three distinct advantages: first, the convergence is better and faster due to update of position by the best and worst individuals; second, the overall diversity of the population is increased due to learning from different individuals avoiding worst individual and third, the exploitation of the search space is improved due to inclusion of personal best of the individual into the evolution. Extensive simulations have been performed on a set of twenty-three standard mathematical benchmark functions, six CEC2005, and fifteen CEC2015 benchmark functions, and one new problem of UAV (Unmanned Aerial Vehicle) planning to test the performance of the proposed algorithm. The inherent advantages of the proposed HDEL facilitate quick response attributes, which is essential in UAV planning for communication. Performance comparison of the proposed HDEL with other state-of-the-art algorithms is also performed and discussed in detail. The results indicate that the proposed HDEL offers a significant improvement in terms of optimum value, convergence speed, and computational complexity in comparison to most of the reported optimization algorithms.
Keywords: Optimization; Evolutionary Algorithms; Differential Evolution; Hybridization.
Hybrid Moth-Flame optimization algorithm with Differential Evolution for visual object tracking
by KUPPIREDDY NARSIMHA REDDY, Bojja Polaiah
Abstract: The improvement of the metaheuristic algorithms is one of the interesting topics to investigators in current years for resolving the optimization problems and constrained engineering problems. One of the population-based search method i.e., Moth-Flame Optimization algorithm (MFO) is eminent by easy execution, low amount of limits and its high speed. On the other hand, the MFO process has shortcomings for instance verdict the local minimum as a substitute of the global minimum and weakness in global pursuit proficiency. In this paper, to resolve these shortages, the MFO algorithm is integrated with Differential Evolution (DE) and proposed a new hybrid method called MFO-DE. The exploration ability of the MFO algorithm is improved and as well, existence trapped in the local minimum is prohibited by via a mixture of the MFO and DE in the MFO-DE algorithm. The proposed algorithm was tested on the set of best known unimodal and multimodal benchmark functions in various dimensions. The obtained results from basic and non-parametric statistical tests confirmed that this hybrid method dominates in terms of convergence and success rate. Furthermore, MFO-DE is applied to visual object tracking as a real-life application. All experimental outcomes, illustrations, and comparative investigation found the MFO-DE algorithm can vigorously track a random target object in many stimulating circumstances than the other trackers successfully.
Keywords: Population-based algorithm; Meta-heuristic; Differential Evolution (DE); Global optimization; Visual object tracking.
Deep learning based mitosis detection using genetic optimal feature set selection
by Lakshmanan B, Anand S
Abstract: Mitosis detection in breast cancer is considered to be a vital factor in cancer progression. The significance of identifying mitotic count will be more helpful to estimate the aggressiveness and proliferation rate of the tumour. The manual mitosis detection process is prone to intra-observer variability and also a challenging task. To alleviate this limitation, we present a deep convolution neural network-based genetic optimizer to detect mitosis signature from histopathology images. In this study, the proposed model is designed to solve the problems of feature dimensionality, computational cost and misclassification rate. The deep learning-based genetic optimizer consists of two phases: first, deep convolutional neural network and second is genetic optimizer. It is compared to state-of-the-art algorithms using MITOS-ATYPIA-14 dataset. The proposed architecture achieved an accuracy of 98.7% with 91% precision, 89% recall and 92% F-score. Results are obtained from experiments conducted on 760 histopathology breast cancer images in which 415 images are used for training and 345 images are taken for testing. Significantly, the proposed model will intelligently assist and help pathologists to do their jobs more efficiently. Finally, the model could help pathologists, medical practitioner to understand the progression of the cancer stages.
Keywords: Breast cancer; histopathology images; convolutional neural network; genetic optimizer; mitosis detection.
Solving systems of nonlinear equations with real world problems using an advanced hybrid algorithm
by Pooja Verma, Raghav Parouha
Abstract: System of nonlinear equations (SNLEs) existed in various disciplines such as engineering, economics, chemistry, mechanics, and applied mathematics. Finding solutions to SNLES is one of the most challenging problems that scholars face, because it cannot be solved as easily as linear systems. An advanced hybrid algorithm (haDEPSO) is proposed in this paper for finding the solution of SNLEs and real world problems, based on multi-population approach. Suggested advanced differential evolution (aDE) and particle swarm optimization (aPSO) integrated with proposed haDEPSO. In aDE a novel, mutation strategy and crossover probability along with slightly changed selection scheme is introduced, to avoid premature convergence effectively. And aPSO comprises of novel gradually varying parameters, to avoid stagnation. Therefore, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. Performance of proposed hybrid haDEPSO as well as its integrating component aDE and aPSO are used to solve 3 benchmark SNLEs and 3 complex real world problems. Several numerical, graphical and statistical experiments have been done to verify performances of the proposed algorithms. Additionally, comparative analysis confirms superiority of the proposed algorithms over many state-of-the-art algorithms.
Keywords: System of nonlinear equations; Real world applications; Meta-heuristics algorithms; Hybrid algorithm.
An Improved Chemical Reaction Optimization Algorithm for the 0-1 Knapsack Problem
by Hamza Salami, Abubakar Bala
Abstract: Knapsack Problems (KPs) are well studied NP-hard problems with numerous real-life applications like capital budgeting, cargo loading, load-shedding in microgrids, and resource allocation in cloud computing. Chemical Reaction Optimization (CRO)
is a recently developed metaheuristic algorithm that works based on the principles of chemical reactions. This paper proposes a CRO-based algorithm for solving the 0-1 knapsack problem (0-1 KP). The proposed algorithm (newCRO) utilizes a novel, optimistic neighborhood search operator and a greedy repair and optimization operator to fix invalid solutions and improve feasible solutions. We test the newCRO on a wide range of large scale 0-1 KP benchmark problems, and its results are compared with five other existing methods to show its superior optimization capabilities.
Keywords: chemical reaction optimization; knapsack problems; metaheuristic algorithms.
Deep Convolutional Neural Network applied to Trypanosoma cruzi Detection in Blood Samples
by André S. Pereira, Leonardo O. Mazza, Pedro C. C. Pinto, José Gabriel R. C. Gomes, Nadia Nedjah, Daniel F. Vanzan, Alexandre S. Pyrrho, Juliana G. M. Soares
Abstract: Chagas Disease is a tropical parasitic disease endemic to Latin America. During the acute phase, standard diagnosis is based on Trypanosoma cruzi visualization through microscopy applied to peripheral blood slides. In the present work, we apply a deep convolutional neural network to the binary classification of image tiles from acute-phase peripheral blood samples. A single-neuron binary classifier receives 1280-dimensional vectors from a MobileNetV2 feature extractor, thus yielding a relatively simple and effective Trypanosoma cruzi detector. In a preliminary experiment using a reduced data set with sample image tiles from 12 slides, we achieve accuracy equal to 96.4\% on a balanced validation subset. On image tiles from a thirteenth blood smear slide, test accuracy is estimated at 72.0%. To improve the accuracy, we extend the data set with images from six additional slides, which include two thick blood samples. Validation accuracy oscillates to 95.5%, but independent tests on two extra slides indicate test accuracy improvement from 72.0% to 95.4%. We provide examples of raster scans with overlapping windows leading to the detection of all positive instances of Trypanosoma cruzi in blood smear and thick blood images, without any false alarm. Furthermore, we start an investigation of the boosting algorithms on the performance of the classification process.
Keywords: Chagas disease; Trypanosoma cruzi; deep convolutional neural networks.
Special Issue on: Bio-inspired Evolutionary Computations and Their Applications
Brushless Direct Current Motor Design Using A Self-adaptive JAYA Optimization Algorithm
by Li Yan, Chuang Zhang, Boyang Qu, Kunjie Yu, Caitong Yue
Abstract: This paper proposes a self-adaptive JAYA (SAJAYA) to maximize the efficiency of the brushless direct current (BLDC) motor by optimizing the design parameters. In SAJAYA, an adaptive weight strategy is introduced into the original JAYA to control the degree of approaching the best solution and avoiding the worst solution at different evolution stages. Moreover, to maintain the diversity and enhance the local search ability, a new hybrid learning strategy is developed. Further, a self-adaptive selection mechanism is designed in order to dispatch the original learning strategy and the new hybrid learning strategy to each individual automatically. Based on this framework, the exploration and exploitation abilities of the population are expected to be balanced. Besides, a chaotic elite search is introduced to further refine the best solution of each generation. Experimental results show that the proposed SAJAYA shows a superior performance in solving the BLDC motor optimization problem compared with other well-established algorithms.
Keywords: Evolutionary computation; JAYA algorithm; Optimization; Brushless DC wheel motor.
Genetic regulatory network-based optimization of master production scheduling and mixed-model sequencing in assembly lines
by Youlong Lv, Jie Zhang, Liling Zuo
Abstract: The integration of master production scheduling and mixed-model sequencing ensures just-in-time production of orders and workload balancing between stations for assembly lines. However, such integrated optimization is complicated because of the high interdependence between these two problems. Based on mathematical model of the integrated optimization problem, a two-level genetic regulatory network is constructed by representing decision variables with gene states and describing multiple objectives and various constraints with gene regulation equations. The solutions are generated through gene expression procedures in which some gene states are activated based on regulation equations, and the optimal one with minimum objective function value is obtained via parameter optimization in regulation equations. The genetic regulatory network-based method is validated under sixteen testing scenarios and applied to the case study of a diesel engine assembly line. The results demonstrate the effectiveness of this method over other ones in realizing integrated optimization.
Keywords: integrated optimization; diesel engine assembly line; master production scheduling; mixed-model sequencing; genetic regulatory network.
A parallel algorithm to solve the multiple traveling salesmen problem based on molecular computing model
by Zhaocai Wang, Tunhua Wu
Abstract: The Multiple Traveling Salesmen Problem (MTSP), which includes m salesmen starting and ending their tours at a same fixed node (m>1), is an extension of Traveling Salesman Problem (TSP) and has more applications and significance in the field of optimal control. As a classic NP-hard static combinatorial optimization problem, its efficient solution has always been the direction of scholars efforts. In this work we propose biocomputing algorithms to solve the MTSP using Adleman-Lipton model. We make use of DNA chains to appropriately represent the nodes, edges and corresponding weights, then efficiently generate all traveling salesmen tours combinations by biochemical reactions. Combining with the nature of the problem, we exclude the infeasible solution strands to get the optimal solutions, and reduce the algorithm computational complexity to O(n^2) level. Meanwhile, the feasibility and practicability of DNA parallel algorithms are verified by theoretical proof and simulation experiments. The proposed algorithms are also helpful to better understand the nature of computing and can be further applied to the study of extended problems.
Keywords: DNA computing; Multiple Traveling Salesmen Problem; Adleman-Lipton model; NP-hard problem.
Techno Economic Analysis Of Novel Multiobjective Quasi-Oppositional Grey Wolf Optimizer
by Gourab Das
Abstract: The optimal DG placement in radial distribution system is an important way for techno-economic analysis. The maximum technical benefits can be extracted by minimizing the distribution power loss as well as bus voltage deviation, whereas the maximum economical benefits can be procured by minimizing the total yearly economic loss which includes installation, operation and maintenance cost. So for the maximum techno-economic benefits, all three objectives should be simultaneously minimized by considering a multiobjective optimization technique. For optimal results, a Pareto optimal concept based a novel multiobjective quasi-oppositional grey wolf optimizer (MQOGWO) algorithm has been proposed. The performance of the proposed algorithm has been tested on IEEE-33 bus radial distribution system. In this analysis, various voltage dependent load models such as constant power, constant current, constant impedance, residential, industrial and commercial load models have been considered at different loading conditions like light load, full load and heavy load. The effects of DG type on the system performance has also been analysed to find the best optimal solution.
Keywords: Distributed Generation; Yearly Economic Loss; Power Loss; Voltage Deviation; Multiobjective Quasi-Oppositional Grey Wolf Optimizer.