International Journal of Bio-Inspired Computation
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International Journal of Bio-Inspired Computation (40 papers in press)
Special Issue on: Artificial Intelligence Facilities Smart Cities Development
Abstract: Real-time people counting based on videos is one of the most popular projects in the construction of smart cities. To develop an accurate people counting approach, deep learning can be used as it greatly improves the accuracy of machine learning based approaches. To this end, we have previously proposed an accurate YOLO (You Only Look Once) based People Counting approach, dubbed YOLO-PC. However, the model of YOLO-PC was very large with an excessive number of parameters, thus it requires large storage space on the device and makes transmission on Internet a time consuming task. In this paper, a new real-time people counting method named as Squeeze YOLO-based People Counting (S-YOLO-PC) is proposed. S-YOLO-PC uses the fire layer of SqueezeeNet to optimize the network structure, which reduces the number of parameters used in the model without decreasing its accuracy. Based on the obtained the experimental results, S-YOLO-PC reduces the number of model parameters by 11.5% and 9% compared to YOLO and YOLO-PC, respectively. S-YOLO-PC can also detect and count people with 41 frames per second (FPS) with the average precision (AP) of person of 72%.
Keywords: Model compression; People counting; Boundary-selection; YOLO; SqueezeNet.
SmartGC: A Software Architecture for Garbage Collection in Smart Cities
by Miguel Ramalho, Rosaldo Rossetti, Nelio Cacho, Arthur Souza
Abstract: With populations in cities increasing in a very accelerated pace, the problem of collecting and handling the waste produced becomes a major concern to governmental authorities. Indeed, the amount of garbage they create is increasing even faster than their populations, worsening the problem and turning garbage collection into a very challenging task. In this paper, we see garbage collection through the spectacles of the emerging concept of Smart Cities, accounting for new performance measures defined on the grounds of sustainability, energy efficiency, optimum resource allocation, and low carbon emission and footprint. We thus devise a smart garbage collection management system, coined SmartGC, whose architecture is detailed and explained. Abstracting out garbage collection from a smart mobility perspective, the underlying methodology supporting the proposed architecture relies on the concept of Artificial Transportation Systems. For the sake of demonstration, we have implemented a routing strategy to generate improved itineraries accounting for the content of garbage containers, which are continuously monitored through IoT-based smart meters. Also, we discuss on how the architecture is instantiated and integrated into the smart city agenda of Natal, a medium-size capital in Northeastern Brazil.
Keywords: smart cities; garbage collection; artificial transportation systems; software architecture.
Time-to-Contact Control: Improving Safety and Reliability of Autonomous Vehicles
by Liang Wang, Berthold K.P. Horn
Abstract: Under traditional car-following control, i.e. human drivers' behavior, the stability condition of traffic system is not satisfied in general. For safety and reliability of autonomous vehicles, additional danger warning system must be used in the adaptive cruise control system to prevent inevitable potential collisions. One reasonable quantity of evaluating potential collisions is time to contact (TTC): how soon will potential collision occur? In this paper, we provide TTC feedback control to improve safety and reliability of autonomous vehicles, and show the effectiveness of TTC feedback. TTC can be estimated by machine vision technics with single uncelebrated camera (i.e. passive sensors). We provide detailed mathematical analysis and algorithmic implementation. The machine vision based TTC algorithm is pretty fast such that the whole system can be implemented on Android smart phones running in real-time. Moreover, it's not trial to estimate relative velocity by differentiating the measured distance between cars with respect to time, because inevitable measurement noise in the distance measurements will be amplified by the derivative operation. The time-to-contact based algorithm provides an alternative approach to estimating the relative velocity, which can also be fused with measurements from other active sensors, if desired.
Keywords: Autonomous vehicles; time to contact (TTC); danger detection; machine visionp; android smartphones.
by Liu Qinming
Abstract: This paper aims to propose a two-phase model integrated patient scheduling and medical device maintenance to improve their reliability, reduce operating costs, and increase operating efficiency. In this paper, one patient scheduling problem with time-window deteriorating maintenance is studied. The objective is to minimize the maximum tardiness of all patients. First, a two-phase mathematical model is developed to characterize the problem. One model is used to solve the lower bound of the number of maintenance activities, and the other is used to obtain the patient scheduling solution. Then, one heuristic is developed for the problem. Finally, numerical experiments can be performed to indicate the efficiency and effectiveness of the proposed methods. The results show that the proposed methods have a better performance for the patient scheduling problem and can be able to obtain one good solution in a short computation time. Few studies have been carried out to integrate decisions between patient scheduling and device maintenance. Their considerations are either incomplete or not realistic enough. A more comprehensive and realistic two-phase model is proposed in this paper.
Keywords: Patient scheduling; Maximum tardiness; Medical device; Time-window deteriorating maintenance; Virtual maintenance.
A New Replica Placement Strategy Based on Multi-objective Optimization for HDFS
by Wang Yang, Zhang Qingfu, Li Yangyang, Dhish Saxena
Abstract: Distributed storage systems like the Hadoop Distributed File System (HDFS) constitute the core infrastructure of cloud platforms which are well poised to deal with big-data. An optimized HDFS is critical for effective data management in terms of reduced file service time and access latency, improved file availability and system load balancing. Recognizing that the file-replication strategy is key to an optimized HDFS, this paper focuses on the file-replica placement strategy while simultaneously considering storage and network load. Firstly, the conflicting relationship between storage and network load is analyzed and a bi-objective optimization model is built, following which a multi-objective optimization memetic algorithm based on decomposition (MOMAD) and its improved version are used. Compared to the default strategy in HDFS, the file-replica placement strategies based on multi-objective optimization provide more diverse solutions. And competitive performance could be obtained by the proposed algorithm.
Keywords: Hadoop; HDFS; replica placement; multi-objective optimization; memetic algorithm.
Adaptive Neighborhood Size Adjustment in MOEA/D-DRA
by Meng Xu
Abstract: The multiobjective optimization algorithms based on decomposition(MOEA/D) is a well-known multiobjective optimization algorithms(MOEAs). MOEA/D was proposed by Zhang and Li in 2007s. MOEA/D decomposes a multiobjective problem into a set of scalar single objective subproblems using the aggregation function and the evolutionary operator. The variant of the dynamic resource allocation strategy in MOEA/D(MOEA/D-DRA) has the outstanding performance on CEC2009, the MOEA/D-DRA using the strategy of resource allocation. It cares about the convergence and ignores the diversity. MOEA/D-DRA is very sensitive to the neighbourhood size. In this paper, we present a new enhanced MOEA/D-DRA strategy based on the adaptive neighbourhood size adjustment(MOEA/D-DRA) to increase the diversity. It focuses on the solutions density around of subproblems. The experiment results demonstrate that MOEA/D-ANA strategy performs the best compared with other five classical MOEAs on the CEC2009 test instances.
Keywords: MOEA/D; diversity; Neighborhood; CEC2009 test instances;.
A privacy-preserving recommendation method based on multi-objective optimization for mobile users
by Chonghuan Xu
Abstract: Recommender systems have proven to be an effective technique to deal with information overload and mislead problems by helping users get useful and valuable information or objects from massive data. However, exploiting users preferences with recommendation algorithms lead to serious privacy risks, especially when recommender service providers are unreliable. An ideal recommender system should be both accurate, diverse and security. In this paper, we propose a private recommendation method which consists of a private collaborative filtering algorithm and a multi-objective evolutionary algorithm for mobile users. Experimental results demonstrate that even though the mobile users preferences are significantly obfuscated, our method is effective in terms of recommendation accuracy and diversity.
Keywords: Recommender systems;Multi-objective optimization;Differential privacy;Mobile users.
Recognition of driver emergency braking behavior based on support vector machine optimized by memetic algorithm
by Shenpei Zhou, Bingchen Qiao, Haoran Li, Bin Ran
Abstract: Surface electromyography (sEMG) is one of the main information sources of human motion detection and has been widely used. The lower limb sEMG signal is introduced into the recognition model of driver emergency braking behavior, and the features from time domain, frequency domain and model parameters are extracted to construct a feature vector. In addition, to improve recognition accuracy, the data from conventional braking and accelerated shifting behaviors similar to the characteristics of emergency braking are synchronously collected. These three driving behaviors are identified by using support vector machine (SVM), and a memetic algorithm (MA) based on particle swarm optimization and hill climbing algorithm is proposed to optimize the parameters of SVM. The results show that the model based on SVM optimized by MA has better classification performance than that without optimization. The final recognition rate of emergency braking behavior of same individual is up to 92.3%, and that of different individuals can reach 85.6%. Moreover, the system can detect emergency braking 220 ms earlier than operating brake pedal. At 100 km/h driving speed, this amounts to reducing the braking distance by 6.1 m.
Keywords: sEMG; emergency braking; SVM; parameter optimization; memetic algorithm; particle swarm optimization; hill climbing algorithm.
A Bee Colony Optimization Algorithm with a Sequential-Pattern-Mining-based Pruning Strategy for the Traveling Salesman Problem
by Shin Siang Choong, Li-Pei Wong, Malcolm Yoke Hean Low, Chin Soon Chong
Abstract: Bees perform waggle dance in order to communicate the information of food source to their hive mates. This unique foraging behaviour has been computationally realized as an algorithmic tool named the Bee Colony Optimization (BCO) algorithm to solve different types of Combinatorial Optimization Problems such as Traveling Salesman Problem (TSP). In order to enhance the performance of BCO, local optimization can be integrated. However, local optimization incurs high processing overhead especially when all solutions are allowed to undergo the local optimization. To reduce the high processing overhead, two existing pruning strategies, i.e. Frequency-based Pruning Strategy (FBPS) (Wong et al., 2009b) and Frequent-closed-pattern-based Pruning Strategy (FCPBPS) (Wong & Choong, 2015) were proposed to prohibit a subset of solutions from undergoing the local optimization. The rationale of these pruning strategies is to allow only the solutions which contain a significant amount of frequent building blocks to perform local optimization. This paper proposes a new pruning strategy based on the top-k sequential patterns mining (TKS) algorithm. Specifically, TKS is employed to identify the frequent building blocks along the optimization process. A total of 19 selected symmetric TSP benchmark problem instances ranging from 318 cities to 1291 cities were used as the test bed of this study. Based on the experimental results, the proposed pruning strategy shows a significant reduction in terms of the computational time to yield TSP solutions with similar tour length as compared with two state-of-the-art approaches.
Keywords: meta-heuristic; local search; data mining; sequential pattern mining; frequency-based pruning strategy; frequent-close-pattern-based pruning strategy; combinatorial optimization.
PSO-MoSR: A PSO based Multi-objective Software Remodularization
by Amarjeet Prajapati, Sanjeev Kumar
Abstract: The quality of modular structure of a software system highly affects the success of a software project. Software remodularization which is used to improve the software structure is a complex task and involves the optimization of multiple conflicting aspects. To address the optimization of multiple objectives, many metaheuristic optimization algorithms have been designed. The customization of these algorithms according to the suitability of real-world multi-objective software remodularization problem is a challenging task. In this article, particle swarm optimization (PSO) a widely used metaheuristic heuristic technique is customized and proposed a PSO based Multi-objective Software Remodularization (PSO-MoSR) to address the optimization of multiple objective issues of software remodularization. The effectiveness of the proposed PSO-MoSR is evaluated by conducting several experiments by modularizing seventeen real-world software systems.
Keywords: Software remodularization; restructuring; optimization; module clustering.
Bio-inspired parameter-less heuristic for NP-hard(complete) discrete problems
by Manal Zettam
Abstract: In this article, a Bio-inspired parameter-less heuristic employs a path-relinking approach coupled with a local search instead of moving alternatives within the search space. In addition, a Mean solution has assured the exploration and exploitation phases. The Bio-inspired parameter-less heuristic has been compared to seventeen algorithms on set of benchmarks. The comparative study shows that the proposed heuristic performs well on 26 benchmarks.
Keywords: NP-complete Problem; Bio-inspired heuristic; path-relinking; parameter-less heuristic.
An Effective User Clustering-based Collaborative Filtering Recommender System with Grey-Wolf Optimization
by Sivaramakrishnan N, Subramaniyaswamy V, Logesh Ravi, Vijayakumar V, Xiao-Zhi Gao, Rakshana Sri S L
Abstract: The enormous amount of data available today often makes it difficult for users to make decisions. Recommendation Systems have become increasingly popular and mainly used in e-commerce to helping predict user preference towards particular items. The proposed system performs user cluster-based collaborative filtering for venue recommendations in which clusters are formed using a bio-inspired grey wolf optimization algorithm. Clustering is used to eliminate the disadvantages of collaborative filtering regarding scalability, sparsity, and accuracy. In addition, we have used two similarity computation methods, namely the Pearson Correlation Coefficient (PCC) and Cosine Similarity to find the similarities between the set of users. The proposed recommendation system with the bio-inspired grey wolf optimization algorithm has been evaluated on real-world massive volume datasets of Yelp and Trip Advisor for finding out the accuracy, precision, recall, and f-measure. We have also modeled and validated new mobile-based recommendation application frameworks for the development of urban venue recommendations in smart cities. The experimental and evaluation results demonstrate the usefulness of the newly generated recommendations and exhibit user satisfaction with the proposed recommendation technique.
Keywords: Recommender System; Grey Wolf Optimization; User Clustering; Venue Recommendation; Similarity; Collaborative Filtering.
Swarm and Evolutionary Algorithms for Energy Disaggregation: Challenges and Prospects
by Samira Ghorbanpour, Trinadh Pamulapati, Rammohan Mallipeddi
Abstract: Energy disaggregation is defined as the process of estimating the individual electrical appliance energy consumption of a set of appliances in a house from the aggregated measurements taken at a single point or limited points. The energy disaggregation problem can be modelled both as pattern recognition problem and as an optimization problem. Among the two, the pattern recognition problem has been considerably explored while the optimization problem has not been explored to the potential. In literature, researchers have attempted to solve the problem using various optimization algorithms including swarm and evolutionary algorithms. However, the focus on optimization-based methodologies, in general, swarm and evolutionary algorithm based methodologies in particular is minimal. By considering the different problem formulations in the literature, we propose a framework to solve the energy disaggregation problem with swarm and evolutionary algorithms. With the help of simulation results using the existing problem formulations, we discuss the challenges posed by the energy disaggregation to swarm and evolutionary algorithm based methodologies and analyse the prospects of these algorithms for the problem of energy disaggregation with some future directions.
Keywords: Energy Disaggregation; Swarm and Evolutionary Algorithms.
An Improved Brain Storm Optimization Algorithm for Energy-efficient Train Operation Problem
by Boyang Qu, Qian Zhou, Yongsheng Zhu, Jing Liang, Caitong Yue, Yuechao Jiao, Li Yan
Abstract: This paper presents a new method to determine the optimal driving strategies of the train using an improved brain storm optimization (IBSO) algorithm. In the proposed method, the idea of successful-parent-selecting frame is applied to improve the original brain storm optimization (BSO) algorithm avoiding premature convergence in evolutionary process while dealing with complex problems. The objective of the algorithm is to minimum energy consumption of the train by nding the switching points. Furthermore, the speed limits, gradients, maximum acceleration and deceleration as well as the maximum traction and braking force varying with speed are taken into consideration to meet practical constraints. Finally the comparison simulations among four algorithms show that the energy-efficient train operation strategy obtained by IBSO algorithm are more superior under same conditions.
Keywords: Evolutionary algorithms; Brain Storm Optimization; Energy-efficient train operation.
Flight Control System Design Using Adaptive Pigeon-Inspired Optimization
by Mostafa Saad, Duan Haibin
Abstract: Pigeon-Inspired Optimization (PIO) algorithm is a swarm intelligence algorithm inspired by homing behavior of pigeons. Adaptive Pigeon-Inspired Optimization (APIO) algorithm is introduced to provide better search efficiency and faster convergence speed than Pigeon-Inspired Optimization (PIO) algorithm. Through (APIO), the optimization algorithm will better deal with the flying vehicles and the optimization process can reach an optimum solution during the control cycle time of the control system rather than Pigeon-Inspired Optimization (PIO) algorithm. In this paper the flight control system for a tactical missile is presented using classical controller (PID), and the controller gains will be calculated using both (PIO) and (APIO) during vehicle flight. Platform simulation is presented to evaluate the performance of both algorithms.
Keywords: Flying vehicle; Tactical missile; Autopilot design; Pigeon-inspired optimization (PIO); Adaptive Pigeon-inspired optimization (APIO); Classical controller (PID); Simulation.
The Improved ColorAnt Algorithm: A Hybrid Algorithm for Solving the Graph Coloring Problem
by Anderson Faustino Da Silva, Luis Gustavo Araujo Rodriguez, Joao Fabricio Filho
Abstract: The Graph Coloring Problem is interesting because of its application areas, ranging from register allocation, frequency association in telecommunications, time tabling and scheduling, and others. This problem is NP-Complete and; thus, several metaheuristic algorithms have been proposed in order to provide a good solution in an acceptable time-frame. Among several metaheuristics, this paper focuses on Ant Colony Optimization. In this context, a hybrid algorithm was developed called iColorAnt, which uses Ant Colony Optimization and an efficient local search strategy, consequently providing good solutions to the Graph Coloring Problem. iColorAnt focuses on three points unexplored by the original ColorAnt algorithm. First, it uses several strategies simultaneously. Second, it uses a memory-based strategy. Third, it adapts itself to specific graphs. The experiment results indicate that iColorAnt outperforms its predecessor, ColorAnt.
Keywords: Graph coloring; metaheuristic; ant colony optimization.
A Routing algorithm based on Simulated Annealing algorithm for Maximizing Wireless Sensor Networks Lifetime with a Sink node
by Hui Wang, Kangshun Li, Witold Pedrycz
Abstract: Energy saving becomes a central issue in the design of wireless sensor network routing algorithms. In the wireless sensor networks (WSNs), when intra-network communication is ensured, the lifetime of node can be extended by reducing data transmission or data volume as much as possible. However, the problem is that energy of the nodes around the Sink node becomes exhausted quickly due to excessive communication overhead. To handle this problem, in this study, we propose a routing algorithm based on the Sink node path optimization. The study uses the energy consumption model as a constraint, transforms the time optimization problem into an optimization model, optimizes the Sink node path with the aid of Simulated Annealing (SA) algorithm, and uses data fusion to reduce the intra-network redundant data in the time domain. The proposed algorithm innovatively self-adjusts the path of Sink node that is optimized by SA using new fitness function. Comprehensive simulation results show that the proposed algorithm can reduce the node energy consumption of waiting of Sink node at the address of Sink node, balance the network load and improve survival time of WSNs by 30% in comparison with results produced with the state-of-the art algorithms REAC-IN and DALMDT.
Keywords: Routing algorithm; Sink node; WSNs; SA; optimal path.
A Two-Lane Mixed Traffic Flow Model with Drivers' Intention to Change Lane Based on Cellular Automata
by Changbing Jiang, Ruolan Li, Tinggui Chen, Chonghuan Xu, Liang Li, Shufang Li
Abstract: Aiming at the deficiencies of classic NaSch model and two-lane lane changing model (STCA), based on the improvement of the rules and lane changing rules, the viaduct is taken as the simulation objective. Under the open boundary condition, a two-lane mixed traffic flow model considering the intention to enter the lane is established. Considering the need of driver to enter lane research, this model puts forward the vehicle enter rules, which takes into account the relevant factors that affect drivers intention to enter the road and quantify the influencing factors through the method of average input rate in queuing theory. Numerical simulation experiment shows that the average input rate ? is determined by input rate ? and output rate ?, and is affected by the sensitivity parameter ?, and ? is not greater than ?. The passing state of a road vehicle is determined by the output rate ? and is affected by ?. At the same time, compared with the model that ignores the drivers intention to enter the road, the model proposed in this paper is closer to the actual traffic situation.
Keywords: Average arrival rate;cellular automata;drivers'intention to change lane;mixed traffic flow.
A Variant of EAM to Uncover Community Structure in Complex Networks
by Tribhuvan Singh, Krishn Kumar Mishra, Ranvijay
Abstract: Environmental Adaptation Method (EAM) was developed to solve single-objective optimization problems. After the first proposal, other variants have been suggested to speed up the convergence rate and to maintain the diversity of the solutions. Among those variants, IEAM-RP works with real numbers. In this paper, a variant of IEAM-RP has been suggested with major changes in adaptation operator to improve the overall performance of the algorithm. In the proposed method, significant attention has been given for balancing exploration and exploitation of individuals in the population. The performance of the proposed algorithm is compared against 14 state-of-the-art algorithms using standard benchmark functions of the COCO (COmparing Continuous Optimisers) framework. It has been observed that the proposed approach is very competitive with other algorithms and it either outperforms or performs similarly with other state-of-the-art algorithms. Further, to check the effectiveness of the proposed approach, it has been applied to a real-world problem of community detection in complex networks. In this problem, modularity optimization is taken into consideration as a fitness function. Again, the experimental results are found very promising and competitive compared to other algorithms.
Keywords: Single Objective Optimization; Evolutionary Algorithms; Environmental Adaptation Method; Community Detection Problem.
Frequency-Dependent Synaptic Plasticity Model for Neurocomputing Applications
by Saad Qasim Khan, Arfan Ghani, Muhammad Khurram
Abstract: In neuroscience, there is substantial evidence that suggests temporal filtering of stimulus by synaptic connections. In this paper, a novel frequency-dependent plasticity mechanism (FDSP) for neurocomputing applications is presented. It is proposed that synaptic junctions could be used to perform bandpass filtering on the input stimulus. The unique transfer function of a bandpass filter replaces the conventional weight value associated with synaptic connections. The proposed model has been simulated and rigorously tested with standard machine learning benchmarks such as XOR and multivariate IRIS dataset while utilising minimum resources. The proposed model offers a unique advantage and has the potential to overcome the burden of hidden layer neurons from the network. Exclusion of hidden layer from the network significantly reduces the size of the network and hence the computational effort required for classification tasks. The proposed FDSP mechanism allows for complete analogue system design with a frequency multiplexed communication scheme. The main goal of this study is to establish frequency-dependent plasticity as an alternative to existing time-domain based techniques. The proposed method has a number of applications in neurocomputing, low power IoT devices and compute-efficient Deep Convolutional Neural Networks (DCNNs).
Keywords: Machine learning; Neural networks; IoT devices; Neural engineering; Data classification; Synaptic plasticity.
An Effective Improved Co-evolution Ant Colony Optimization Algorithm with Multi-Strategies and Its Application
by Wu Deng, Huimin Zhao
Abstract: In this paper, an effective improved co-evolution ant colony optimization(MSICEAO) algorithm is presented to solve complex optimization problem. In the MSICEAO, the multi-population co-evolution strategy is used to divide initial population into several sub-populations to interchange and share information. The weighted initial pheromone distribution strategy is used to improve the efficiency and adjust the pheromone factor and distance factor. The elitist retention strategy is used to improve the solution quality. The adaptive dynamic update strategy for pheromone evaporation rate is used to balance the convergence speed and solution quality. The aggregation pheromone diffusion mechanism is used to enhance the cooperative effect and highlight the cooperative idea of swarm intelligence. In order to verify the effectiveness of the MSICEAO, the experiments have been carried out on 8 TSPs and one actual gate allocation problem. The MSICEAO is compared with five state-of-the-art algorithms of TS, GA, PSO, ACO and PSACO. The experiment results demonstrate that the MSICEAO is significantly better than the compared methods.
Keywords: Ant colony optimization; Multi-population co-evolution; Elitist retention; Pheromone distribution and diffusion; Adaptive dynamic update; Gate allocation.
Multi-objective optimization framework of genetic programming for investigation of Bullwhip effect and Net Stock Amplification for three-stage supply chain systems
by Akhil Garg, Surinder Singh, Liang Gao, Xu Meijuan
Abstract: To address the concerns on large variance in demand orders and stocks at inventory level (referred as bullwhip effect and net stock amplification (NSA)), the methods based on reducing uncertainty, variability, lead times and forming strategic partnerships can be applied. However, these methods could not eliminate the effect entirely but can only reduce it. The four input factors mainly affecting the bullwhip effect and NSA includes the demand signal processing, batch order, lead time and rationing and shortage gaming. The research problem of studying the impact of these four input factors on the bullwhip effect and NSA, and determining the key trade-off between them can be solved by the notion of mathematical modeling. Therefore, this work will propose a multi-objective optimization framework of genetic programming (GP) in the modeling of bullwhip effect and NSA for centralized and decentralized supply chain systems. The individual and interactive effect of these four input factors has been investigated on bullwhip effect and NSA by adapting the parametric and sensitivity approach on the formulated models. The appropriate settings of dominant input factors (batch ordering and demand signal processing for a decentralized chain, demand signal processing and rationing shortage gaming for a centralized chain) are suggested to optimize the bullwhip effect and NSA of three-stage supply chain simultaneously. The implications and advantages of proposed optimization framework will be useful for business practitioners to monitor and supervise the sudden demand amplification that generally faced by them in the supply chains.
Keywords: bullwhip effect; net stock amplification; genetic programming; modeling; optimization.
Inspiration-wise Swarm Intelligence Meta-heuristics for Continuous Optimization: A Survey--- Part I
by Nedjah Nadia, Luiza Mourelle, Rienaldo Moraisn
Abstract: Optimization is a problem found in many sciences such as engineering, administration, transportation, economics and biology, among others. Global optimization techniques aim to find the best solution in a set of feasible solutions to a problem. Currently, there are numerous optimization techniques. In general, the problems to be optimized are complex, nonlinear and in some cases may be intractable. Meta-heuristics are general algorithmic frameworks adaptable to various optimization problems and are generally applied to highly complex problems. Swarm Intelligence represents intelligent models inspired by real-world social systems, based on interaction and organization between simple agents to perform simple tasks. Nowadays, there are many swarm based meta-heuristics. The inspiration behind the strategies can allow us to propose a kind of taxonomy for the current state-of-the-art of swarm-oriented optimization methods. The overall survey, which will be divided into three separate parts, provides a review of swarm based meta-heuristic, which are commonly employed to solve complex continuous optimizations, aiming at building a inspiration-based taxonomy for such search strategies. In this part of the survey, we review meta-heuristics that are guided by some human relation\'s characteristics and those that are inspired by physical system\'s properties.
Keywords: Swarm Intelligence; swarm-based meta-heuristics; Bio-inspired computation.
Nano-indentation Analysis Comparing Dragonfly-inspired Biomimetic Micro Aerial Vehicle (BMAV) Wings
by Erfan Salami, Thomas Arthur Ward, Elham Montazer, Nik Nazri Nik Ghazali
Abstract: Biomimetic micro-air vehicles (BMAV) are micro-scaled, unmanned aircraft based on flying biological organisms, generating thrust and lift by flapping their wings. This study investigates and compares the Nano mechanical elastic properties of four sets of fabricated, dragonfly inspired BMAV wings and compares them to actual dragonfly wings, used as a baseline reference. Since it is extremely difficult to fabricate the complex microstructures of an actual dragonfly wing, the fabricated wings are simplified replications (forewing and hindwing) of the actual wing set. This simplification was performed using the spatial network analysis method. The BMAV wings were fabricated using a 3D printer, based on these simplified models. Different 3D printer filament materials were used for each of the four wing sets: acrylonitrile butadiene styrene (or ABS), polylactic acid (or PLA), high impact polystyrene (or HIPS) as well as Ultrat. Nanoindentation tests of the actual dragonfly wings and the BMAV wings were conducted to measure their hardness and Youngs modulus. Analysis of these measurements form a structural engineering basis that is important for future BMAV wing design. The test result demonstrates the feasibility solution in the development of strong, practical and low cost BMAV wings, this work is a stepping-stone on the path to flying robotic dragonfly.
Keywords: dragonfly; biomimetic; artificial wing structure; tensile stress; nanoindentation.
A Swarm Intelligence Labour Division Approach to Solving Complex Area Coverage Problems of Swarm Robots
by Renbin Xiao, Husheng Wu, Liang Hu, Jinqiang Hu
Abstract: The complex area coverage problem is classical and widespread in the research field of swarm robots. In order to solve the complex area coverage problem with complex non-linear boundary and special task area (forbidden area or threat area), firstly, the task area is adjusted and grid discretization. Then, inspired by the labour division phenomenon of typical biological groups such as bee colony and ant colony, the paper analyzes the performance characteristics of typical ant colony labour division model (response threshold model) and bee colony labour division model (activation-inhibition model) from the perspectives of individual and environment, individual and individual, and a new swarm intelligence labour division approach (activation-inhibition response threshold algorithm) to solve the complex area coverage problem of swarm robot. Three experiments are carried out to illustrate that the algorithm are endowed with great ability of area coverage and dynamic environment. It can respond to the sudden threat in time and make an efficient response, which has a good practical application prospects.
Keywords: area coverage; swarm robot; swarm intelligence; labour division; response threshold model; activation-inhibition model.
Forward Feature Extraction from imbalanced microarray datasets using Wrapper based Incremental Genetic Algorithm
by Devi Priya Rangasamy, Sivaraj Rajappan
Abstract: Learning from imbalanced datasets is a critical challenge confronting researchers. Unequal distribution of classes in the imbalanced datasets lead to biased classification es-pecially in microarray gene expression analysis. Another important concern for researchers is that the microarray datasets usually contain huge number of features with few samples. Since all features in the dataset will not contribute to the analysis, only prominent and sig-nificant features need to be identified. The paper addresses both these issues by proposing Wrapper based Incremental Genetic Algorithm (IGA) which incrementally evaluates and adds attributes into the Genetic Algorithm process rather than evaluation of all attributes thereby reducing the computational complexity and number of features used and improving the measures like classification accuracy, GMean, F1 measure, precision and recall. The experiments are conducted on 8 microarray gene expression datasets and the results show that performance of IGA is encouraging and superior to existing methods that are compared.
Keywords: Incremental Genetic Algorithm; Imbalanced dataset; Bootstrap sampling; Forward Feature Extraction; Adaptive Mutation.
A path planning method for UAV based on multi-objective pigeon-inspired optimization and differential evolution
by Bingda Tong
Abstract: Inspired by the behavior of pigeon flocks, an improved method of path planning and autonomous formation for unmanned aerial vehicle based on the pigeon-inspired optimization and differential evolution is proposed in this paper. Firstly, the mathematical model for UAV path planning is devised as a multi-objective optimization with three indices, i.e., the length of a path, the sinuosity of a path, and the risk of a path. Then the method integrated by pigeon-inspired optimization and mutation strategies of differential evolution is developed to optimize feasible paths. Besides, Pareto dominance is applied to select global best position of a pigeon. Finally, a series of simulation results compared with standard particle swarm optimization algorithm and standard differential evolution algorithm show the effectiveness of our method.
Keywords: path planning;unmanned aerial vehicle;pigeon-inspired optimization;differential evolution.
Enhanced Pigeon Inspired Optimization Approach for Agile Earth Observation Satellite Scheduling
by Shang Xiang, Lining Xing, Ling Wang, Yongquan Zhou, Guansheng Peng
Abstract: The agile earth observe satellite scheduling problem has been one of the most difficult problem in the field of operations research. For agile satellite, a different observation time means a different observing angle, thus defining a different transition time from the adjacent tasks. Therefore, the agile earth observe satellite scheduling problem has a features of time-dependent which make the solving more difficult. This article formulated a new model of agile earth observe satellite scheduling problem and designed a new solving frame. Enhanced pigeon-inspired optimization was proposed for solving it, in which guide operator and accelerate operator are introduced to enhance the search capability of the algorithm. After test on 15 experimental scenarios, the proposed algorithm own a better performance than the state-of-the-art algorithm.
Keywords: Agile satellite scheduling; pigeon-inspired optimization; guide operator; accelerate operator.
An Improved Discrete Pigeon-Inspired Optimization Algorithm for Flexible Job Shop Scheduling Problem
by Xiuli Wu, Xianli Shen, Ning Zhao
Abstract: The pigeon-inspired optimization (PIO) algorithm, which is a new promising optimization algorithm, has successfully solved many continuous optimization problems. In the literature, however, little research has been conducted on its application to the combinational optimization problems. This paper therefore tries to fill in this gap and applies the PIO algorithm to solve the flexible job shop scheduling problem (FJSP), which is a typical combinational optimization problem. It proposes an improved discrete PIO (IDPIO) algorithm to minimize the makespan of FJSP and develops methods to optimize the time to carry out the map and compass operator or the landmark operator with the PIO. The discrete map, compass operator, and the discrete landmark operator are developed respectively. The experiment results show that the IDPIO algorithm can solve the FJSP effectively and efficiently.
Keywords: Discrete pigeon-inspired optimization algorithm; Flexible job shop scheduling problem; Discretization; Map and compass operator; Landmark operator.
Computer Assisted Medical Decision Making System using Genetic Algorithm and Extreme Learning Machine for Diagnosing Allergic Rhinitis
by Elgin Chrsito V.R, H. Khanna Nehemiah, Kindie Nahato, Brighty J, Kannan Arputharaj
Abstract: Allergic Rhinitis (AR) is an antigen-mediated inflammation of the nasal mucosa that might extend into the paranasal sinuses. Rhinorrhea, nasal obstruction or blockage, nasal itching, sneezing, and postnasal drip that reverse spontaneously or after treatment are symptoms of AR. Allergic conjunctivitis frequently accompanies AR. For diagnosis of AR, intradermal skin tests remain the gold standard. This paper presents a clinical decision-making system that assists the clinicians to diagnose whether a patient suffers from AR. Feature selection is done using a wrapper approach that employs genetic algorithm (GA) and Extreme Learning Machine (ELM). The selected features are trained and tested using an ELM classifier. For experimenting, the outcome of the symptoms observed in 872 patients for diagnosing the presence or absence of AR has been used. The experimental result shows that the system has achieved an accuracy of 97.7%.
Keywords: Genetic Algorithm; Extreme Learning Machine; Computer assisted decisionrnmaking; Allergy and Immunology; Machine learning.
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.
Bee-inspired Task Allocation Algorithm for Multi-UAV Search and Rescue Missions
by Heba Kurdi, Shiroq Al-Megren, Ebtesam Aloboud, Abeer Alnuaim, Hessah Alomair, Reem Alothman, Alhanouf Ben Muhayya, Noura Alharbi, Manal Alenzi, Kamal Youcef-Toumi
Abstract: Task allocation plays a pivotal role in the optimization of multi-unmanned aerial vehicle (multi-UAV) search and rescue (SAR) missions in which the search time is critical and communication infrastructure is unavailable. These two issues are addressed by the proposed BMUTA algorithm, a bee-inspired algorithm for autonomous task allocation in multi-UAV SAR missions. In BMUTA, UAVs dynamically change their roles to adapt to changing SAR mission parameters and situations by mimicking the behavior of honeybees foraging for nectar. Four task allocation heuristics (auction-based, max-sum, ant colony optimization, and opportunistic task allocation) were thoroughly tested in simulated SAR mission scenarios to comparatively assess their performances relative to that of BMUTA. The experimental results demonstrate the ability of BMUTA to achieve a superior number of rescued victims with much shorter rescue times and runtime intervals. The proposed approach demonstrates a high level of flexibility based on its situational awareness, high autonomy, and economic communication scheme.
Keywords: Task Allocation; Bio-Inspired Algorithms; Unmanned Aerial Vehicles; Distributed Systems; Search and Rescue; Optimization Problems.
Inspiration-wise Swarm Intelligence Meta-heuristics for Continuous Optimization: A Survey--- Part II
by Nedjah Nadia, Luiza De Macedo Mourelle, Reinaldo Morais
Abstract: Optimization is a a tool required in many sciences such as engineering, administration, logistics, transportation, economics and biology, among others. Global optimization techniques aim to find the best solution in a set of feasible ones. Meta-heuristics are general algorithmic frameworks adaptable to various optimization problems. In generally, these are applied to highly complex problems. Swarm Intelligence represents intelligent models inspired by existing social systems, based on interaction and organization between simple individuals to perform simple tasks. In the literature, there are many interesting swarm based meta-heuristics. It always is useful to have a taxonomy for the current state-of-the-art of swarm-oriented meta-heuristics. In the first part of this survey, we proposed an extensible such a taxonomy that is based on the inspiration behind the search strategy of the technique.\r\nThis work as a whole provides a survey of swarm based meta-heuristic, which are commonly employed to solve complex continuous optimizations, aiming at building a inspiration-based taxonomy for such search strategies. In this second part of the survey, we propose a review of existing swarm-oriented search strategies, categorized according to fictional metaphors\' inspiration as well as flocking and schooling inspirations, which in turn are considered to be bio-inspired. For each of the included technique, we explain the essence of the swarm search strategy.
Keywords: Swarm Intelligence; Swarm-based meta-heuristics; Bio-inspired computation.
Formation control of multiple UAV’s via pigeon swarm optimization
by BAI 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
Archived elitism in evolutionary computation: towards improving solution quality and population diversity
by Maxim A. Dulebenets
Abstract: Many evolutionary algorithms, developed for solving complex optimisation problems, deploy the elitist strategy. The elitist strategy ensures that a group of the fittest individuals will be transferred to the next generation before performing any algorithmic operations. In general, elitism allows improving the algorithmic performance in terms of solution quality. However, transferring a group of the fittest individuals to the next generation will increase the selection pressure and significantly limit chances of the newly created offspring chromosomes to survive. In order to address the latter drawbacks, this study proposes and evaluates a number of alternative archive-based elitist strategies, where the fittest individuals are stored in the archive and transferred from that archive into the population based on certain rules. The computational experiments are conducted for the unrelated machine scheduling problem, where the total job processing cost is minimised. The results indicate that the proposed 'strong archived elitism' strategy, which samples the best individual discovered from the archive in every generation, outperforms the other elitist strategies in terms of the objective function values by up to 8.29% over the considered problem instances. Moreover, the 'strong archived elitism' strategy improves the population diversity, which further facilitates the explorative capabilities of the algorithm.
Keywords: optimisation; elitist strategies; machine scheduling problems; MSPs; evolutionary computation; population diversity; strong archived elitism; solution quality.
Variable-grouping-based exponential crossover for differential evolution algorithm
by Shu Yang, Qiuling Huang, Laizhong Cui, Kunkun Xu, Zhong Ming, Zhenkun Wen
Abstract: The performance of differential evolution (DE) algorithm largely depends on its crossover operator, whose substantive characteristics are to make the algorithm search in a subspace of the original search space. Different crossover operators use different subspace divisions, and how to choose a suitable crossover operator for a specific optimisation problem is still an open issue. This paper proposes variable-grouping-based exponential crossover (VGExp), where all variables are divided into multiple groups based on interaction information, and the variables that are mutated simultaneously have a high probability of coming from the same group. Moreover, the solutions can improve the accuracy of the variable grouping and provide initial guidance for optimisation. Therefore, the proposed VGExp seamlessly combines variables grouping technique and differential evolution. The experiment results based on 30 CEC2014 test problems show that VGExp can improve the performance of most DE variants, and it is also better than other well-developed crossover operators.
Keywords: differential evolution; exponential crossover; variable grouping; variable interaction.
Bat algorithm with Weibull walk for solving global optimisation and classification problems
by Hafiz Tayyab Rauf, Muhammad Hadi, Abdur Rehman
Abstract: Bat algorithm (BA) becomes the most widely employed meta-heuristic algorithm to interpret the diverse kind of optimisation and real-world classification problems. BA suffers from one of the influential challenges called local minima. In this study, we carry out two modifications in the original BA and proposed a modified variant of BA called bat algorithm with Weibull walk (WW-BA) to solve the premature convergence issue. The first modification involves the introduction of Weibull descending inertia weight for updating the velocity of bats. The second modification approach updates the local search strategy of BA by replacing the Random walk with the Weibull Walk. The simulation performed on 19 standard benchmark functions represent the competence and effectiveness of WW-BA compared to the state of the art techniques. The proposed WWBA is also examined for classification problem. The empirical results reveal that the proposed technique outperformed the classical techniques.
Keywords: bat algorithm; premature convergence; exploration; exploitation; Weibull walk; inertia weight.
Data-driven pollution source location algorithm in water quality monitoring sensor networks
by Xuesong Yan, Chengyu Hu, Victor S. Sheng
Abstract: Water pollution prevention has been a widely concerned issue for the safety of human lives. To this end, water quality monitoring sensors are introduced in the water distribution systems. Due to the limited budget, it is impossible to deploy sensors everywhere but a small number of sensors are deployed. From the sparse sensor data, it is important, but also challenging, to find out the pollution source location. Traditional methods may suffer from local optimum trapping or low localisation accuracy. To address such problems, we propose a cooperative intelligent optimisation algorithm-based pollution source location algorithm, which is a data-driven approach in simulation-optimisation paradigm. Through open-source EPANET simulator-based experiments, we find out our proposed data-driven algorithm can effectively and efficiently localise the pollution location, as well as the pollution injection starting time, duration and mass.
Keywords: sensor networks; pollution source location; simulation optimisation; cooperative optimisation algorithm.
An enhanced breeding swarms algorithm for high dimensional optimisations
by Jon A. Hansen, Jørgen Sund, Dylan Tollemache, Ali Arefi, Ghavameddin Nourbakhsh
Abstract: This paper proposes a metaheuristic optimisation algorithm named enhanced breeding swarms (EBS), which combines the strengths of particle swarm optimisation (PSO) with those of genetic algorithm (GA). In addition, EBS introduces three modifications to the original breeding swarms to improve the performance and the accuracy of the optimisation algorithm. These modifications are applied on the acceptance criteria based on the improved glowworm swarm optimisation, velocity impact factor, and the mutation operator. The EBS algorithm is tested and compared against GA, PSO, and original BS algorithms, using unrotated and rotated six recognised optimisation benchmark functions. Results indicate that the EBS outperforms GA, PSO, and BS in most cases in terms of accuracy and speed of convergence, especially when the dimension of optimisation increases. As an application of the proposed EBS algorithm, a load flow analysis on a 6-bus network is performed, and the comparison results against another heuristic algorithm and the Newton-Raphson are reported.
Keywords: enhanced breeding swarms; EBS; particle swarm optimisation; PSO; generic algorithm; metaheuristic; improved glowworm swarm optimisation; IGSO; computational intelligence.
Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis
by Xiao Zhang, Enrique Onieva, Asier Perallos, Eneko Osaba
Abstract: Accurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems involves large number of input variables for training and produces a large number of fuzzy rules, which lead to high complexity and barely acceptable accuracy. In this paper, we present a genetic optimised serial hierarchical FRBS, which incorporates lateral tuning of membership functions and optimisation of the rule base. The serial hierarchical structure of FRBS allows selecting and ranking the input variables, which reduces the system complexity and distinguish the importance of attributes in datasets. We conduct an experimental study on Original Wisconsin Breast Cancer Database and Wisconsin Breast Cancer Diagnostic Database from UCI Machine Learning Repository, and show that the proposed system can classify breast cancer accurately and efficiently.
Keywords: genetic algorithm; fuzzy logic; classification system; breast cancer diagnosis; variable selection.