Forthcoming and Online First Articles

International Journal of Bio-Inspired Computation

International Journal of Bio-Inspired Computation (IJBIC)

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International Journal of Bio-Inspired Computation (11 papers in press)

Regular Issues

  • A novel binary multi-swarms fruit fly optimization algorithm for the 0-1 multidimensional knapsack problem   Order a copy of this article
    by Xin Du, Jiawei Zhou, Youcong Ni, Wentao Liu, Ruliang Xiao, Xiuli Wu 
    Abstract: To improve solution quality and accelerate convergence speed of traditional fruit fly optimization algorithm in solving MKP, a novel binary multi-swarm fruit fly optimization algorithm (bMFOA) is proposed. It comprises four novelties. Firstly, an item frequency tree (IFT) is constructed based on the idea of frequency pattern mining, and a new search strategy is proposed to obtain heuristic information. Secondly, two new heuristic operators of "ADD" and "DROP" are designed according to the obtained heuristic knowledge. Thirdly, a multi-swarm cooperation strategy is presented to strengthen the exploitation capability. To prevent algorithm falling into local optimum prematurely, a swarm location escape strategy is put forward. To verify the efficiency of bMFOA, it is compared with some existing meta-heuristic methods by solving 58 MKPs from ORLIB. The experimental results show that the bMFOA performs better than existing meta-heuristic methods.
    Keywords: Fruit fly optimization;Multidimensional knapsack problem; Binary optimization.
    DOI: 10.1504/IJBIC.2021.10047361
     
  • A novel rough semi-supervised k-means algorithm for text clustering   Order a copy of this article
    by Lei-yu Tang, Zhen-hao Wang, Shu-dong Wang, Jian-cong Fan, Guo-wei Yue 
    Abstract: In view of the fact that many attribute values of high-dimensional sparse text data are zero, we combine the approximation set of rough set theory with the semi-supervised k-means algorithm to propose a rough set-based semi-supervised k-means (RSKmeans) algorithm. Firstly, the proportion of non-zero values is calculated by a few labeled data samples, and a small number of important attributes in each cluster are selected to calculate the clustering centers. Secondly, the approximation set is used to calculate the information gain of each attribute. Thirdly, the different attribute values are partitioned into the corresponding approximate sets according to the information gain. Then the new attributes are increased and the above process are continued to update the clustering centers. The experimental results on text data show that RSKmeans algorithm can find those important attributes, filter the invalid information, and improve the performances significantly.
    Keywords: rough set; approximation set; k-means algorithm; semi-supervised clustering; high dimensional sparse data.

  • Recognition of crop leaf diseases based on multi-feature fusion and evolutionary algorithm optimization   Order a copy of this article
    by Lixia Zhang, Kangshun Li, Yu Qi 
    Abstract: Crop leaf disease identification refers to automatically recognize crop leaf pictures suffering from disease, to determine the type of diseases, which is important for agricultural production. Much progress has been made in this field, but there are still many challenges. For example, there are not enough ideal schemes for either disease spot area segmentation or feature representation and matching. In order to meet these challenges, a new crop leaf disease recognition method was proposed in this paper. First, disease spot segmentation method combined ultra-green feature and threshold segmentation was presented. Then, feature representation scheme with multiple features was proposed, which combined color, texture, and shape features. Finally, evolutionary algorithm was used to optimize similarity function for feature matching. Experimental results show that the scheme proposed in this paper can effectively improve recognition accuracy and has a certain practical value.
    Keywords: Crop leaf disease recognition; Evolutionary algorithm; Disease spot area segmentation; Feature representation; Feature matching.

  • Coke price prediction approach based on dense GRU and opposition-based learning salp swarm algorithm   Order a copy of this article
    by Xuhui Zhu, Pingfan Xia, Qizhi He, Zhiwei Ni, Liping Ni 
    Abstract: Coke price prediction is critical for smart coking plants to make sensible production plan. The prediction of coke price fluctuations is a time-series problem, and gated recurrent unit (GRU) performs well on dealing with it. Meanwhile, densely connected GRU can improve the information flow of time-series data, but its key parameters are sensitive. Therefore, a novel coke price prediction method, named DGOLSCPP, is proposed using dense GRU (DGRU) and opposition-based learning salp swarm algorithm (OLSSA). Firstly, a model with two layer stacked dense GRU is constructed for capturing deeper features. Secondly, OLSSA is proposed by introducing opposition-based learning, following and stochastic walk operation for enhancing searching ability. Finally, OLSSA is employed to adjust the key parameters of DGRU for winning the accurate predictive results. Experimental results on two real-world coke price datasets from a certain smart coking plant suggest DGOLSCPP outperforms other competitive methods.
    Keywords: Salp swarm algorithm; GRU; Dense network; Coke price prediction.
    DOI: 10.1504/IJBIC.2022.10047653
     
  • Adaptive Surrogate-based Swarm Intelligence Algorithm and Its Application in Wastewater Treatment Processes   Order a copy of this article
    by Jing Jie, Rui Dai, Hui Zheng, Miao Zhang, Lu Lu 
    Abstract: To solve the computationally expensive optimization problems effectively, a surrogate-based swarm intelligence algorithm is presented. A global surrogate model and a local one are respectively built to approximately evaluate the fitness values of the individuals instead of the accurate function during the evolution process. Following that, the control strategy of swarm evolution and the surrogate management are designed in detail to improve the efficiency of the algorithm. The performance of the proposed algorithm is observed based on comparative experiments with notable benchmark problems and computationally expensive wastewater treatment processes (WWTP). The experimental results prove that the proposed algorithm keeps the trade-off between not only the global and local search, but also the evaluation cost and convergence, which is suitable for computationally expensive optimization problems.
    Keywords: Swarm Intelligence; Surrogate Model; Particle Swarm Optimization; Gaussian Process; Wastewater Treatment Processes (WWTP).

  • Bio-inspired Algorithms for Cybersecurity   Order a copy of this article
    by Kwok Tai Chui, Ryan Wen Liu, Mingbo Zhao, Xinyu Zhang 
    Abstract: It is witnessed that the popularity of the research in cybersecurity using bio-inspired algorithms (a key subset of natural algorithms) is ever-growing. As emergent research area, researchers have devoted efforts in applying and comparing various bio-inspired algorithms to the cybersecurity applications. It is in need to have a systematic review on the bio-inspired algorithms for cybersecurity to fill the gap of missing research study in this topic. The research contributions of this review article are four-fold. It first highlights the foundation of the baseline and latest development of 12 popular bio-inspired algorithms in three categories namely ecology-based, evolutionary-based and swarm intelligence-based algorithms. A systematic review is conducted to synthesise and compare the research methodologies, results and limitations. In-depth discussion will be made on the shortlisted and highly cited articles. The tips to select appropriate algorithm or the combination of multiple algorithms have been reported, along with the pros and cons on the design and formulations. Future research directions will be presented to meet the trends and unexplored research.
    Keywords: bio-inspired algorithms; cybersecurity; ecology-based algorithm; machine learning; evolutionary algorithms; multi-objective optimisation; swarm intelligence; trade-off solution.
    DOI: 10.1504/IJBIC.2023.10048934
     
  • Deep Recurrent Neural Network-Based Hadoop Framework for COVID Prediction with Applications to Big Data in Cloud Computing   Order a copy of this article
    by Jagannadha Rao Db, Vijayakumar Polepally, Nagendra Prabhu S, Parsi Kalpana 
    Abstract: This paper proposes a Particle Squirrel Search Optimization-based Deep Recurrent Neural Network (PSSO-based DRNN) to predict the coronavirus epidemic (COVID). Here, the cloud-based Hadoop framework is used to perform the prediction process by involving the mapper and reducer phases. Initially, the technical indicators are extracted from the time series data. Then, the Deep Belief Network (DBN) is employed for feature selection from the technical indicators. After that, the COVID prediction is done by the DRNN classifier, trained using the PSSO algorithm. The PSSO is developed by the integration of Particle Swam Optimization (PSO) and Squirrel Search Algorithm (SSA). The PSSO-based DRNN is compared with existing methods and obtained minimal MSE and RMSE of 0.0523, and 0.2287 by considering affected cases. By considering death cases, the proposed method achieved minimal MSE and RMSE of 0.0010, and 0.0323 and measured minimum MSE of 0.0049 and minimum RMSE of 0.0702 for recovered cases.
    Keywords: COVID-19; MapReduce; cloud; Deep Belief Network; Deep Recurrent Neural Network.

  • Alligator Optimisation algorithm for solving unconstrainted optimisation problems   Order a copy of this article
    by Weng-Hooi Tan, Junita Mohamad-Saleh 
    Abstract: Inspired by cooperative hunting skills and movement patterns of alligators in nature, this research paper proposes a novel bio-inspired meta-heuristic algorithm, named alligator optimisation (AgtrO) algorithm. Upon mathematical modelling, AgtrO emphasises two main phases: the hunting phase that mimics fishing, purse seining and catching mechanisms, and the relocating phase that mimics travelling and homing instinct mechanisms. The hunting phase discovers any promising global optimal area, towards tracking the true global optimal solution. Meanwhile, the relocating phase avoids local optima (traps) through local exploration and conducts in-depth investigations through local exploitation. The proposed AgtrO was tested on 23 classical optimisation benchmark functions and ten modern CEC-C06-2019 benchmark functions, in comparison with eight recently proposed state-of-the-art algorithms. Upon evaluation, AgtrO has been proved to outperform other algorithms in terms of global-best achievement, while being very competitive in terms of convergence speed.
    Keywords: bio-inspired; metaheuristic; optimisation; classical benchmark; CEC benchmark.
    DOI: 10.1504/IJBIC.2022.10049859
     
  • An algorithm of finding rules for a class of cellular automata   Order a copy of this article
    by Lei Kou, Fangfang Zhang, Luobing Chen, Wende Ke, Quande Yuan, Junhe Wan, Zhen Wang 
    Abstract: A cellular automata (CA) is an important modelling paradigm for complex systems. In the design of CA, the most difficult task is to find the transformation rules that describe the temporal evolution or pattern of a modelled system. A CA with weights (CAW) yields transition rules algorithm is proposed in this paper, which has ample physical meanings and extend the category of CA. Firstly, the weights are increased to connect the updated cell and its neighbours, and the output of each cell depends on the states of cells in the neighbourhood and their respective weights. Secondly, the error correction algorithm is adopted to find correct transition rules by adjusting weights. When the error is zero, the required transition rules with correct weights will be found to describe the fixed configuration. The CAW with the correct rules will relax to the fixed configuration regardless of the initial states. Finally, the mathematical analysis and simulation are carried out with one-dimensional CAW, and the results show that the proposed algorithm has the ability to find correct transition rules as the error converges exponentially.
    Keywords: cellular automaton with weights; CAW; transition rules; updated cells; fixed configuration.
    DOI: 10.1504/IJBIC.2022.10050857
     
  • A hybrid algorithm for workflow scheduling in cloud environment   Order a copy of this article
    by Tingting Dong, Li Zhou, Lei Chen, Yanxing Song, Hengliang Tang, Huilin Qin 
    Abstract: The advances in cloud computing promote the problem processing speed. The number of computing resources in cloud play a vital role in solving user demands, which can be regarded as workflows. More efficient workflow scheduling is a challenge to reduce the task execution time and cost. In recent years, deep reinforcement learning algorithm has been used to solve various combinatorial optimization problems. However, the trained models often have volatility and can not be applied in real situation. In addition, evolutionary algorithm with a complete framework is a popular method to tackle the scheduling problem. But, it has a poor convergence speed. In this paper, we propose a hybrid algorithm to address the workflow scheduling problem, which combines deep reinforcement algorithm and evolutionary algorithm. The solutions generated by deep reinforcement learning are as the initial population in the evolutionary algorithm. Results show that the proposed algorithm is effective.
    Keywords: deep reinforcement learning; evolutionary algorithm; workflow scheduling.

  • Application of Cohort Intelligence Algorithm for Goal Programming Problems with Improved Constraint Handling Method   Order a copy of this article
    by Aniket Nargundkar, ANAND KULKARNI 
    Abstract: Goal programming (GP) is a satisficing-based mathematical modelling technique. In this paper, cohort intelligence (CI) algorithm and its variations are applied to solve a variety of GP problems. The penalty function-based and probability-based constrained handling approaches are applied. Furthermore, a hybridisation of PF and prob-based approaches is developed to handle hard constraints effectively. The proposed approach is validated by solving five benchmark problems as well as practically important real-world truss design, welding beam design, metal cutting, supplier selection, capital budgeting, and staff scheduling problems. The solutions are compared with evolutionary algorithms and LINGO. The results obtained are exceedingly better in terms of satisfying the hard constraints as well as minimising the deviations from the set goals. It is important to note that for truss design, metal cutting and supplier selection problems, all the hard constraints are satisfied using the proposed technique as against with the SA, PSO & Tabu Search.
    Keywords: metaheuristics; goal programming; cohort intelligence; constraint handling.
    DOI: 10.1504/IJBIC.2022.10053699