Multi-leader PSO (MLPSO): A new PSO variant for solving global optimization problems

Abstract Particle swarm optimization (PSO) has long been attracting wide attention from researchers in the community. How to deal with the weak exploration ability and premature convergence of PSO remains an open question. In this paper, we modify the memory structure of canonical PSO and introduce the multi-leader mechanism to alleviate these problems. The proposed PSO variant in this paper is termed as multi-leader PSO (MLPSO) within which the modified memory structure provided more valuable information for particles to escape from the local optimum and multi-leader mechanism enhances diversity of particles' search pattern. Under the multi-leader mechanism, particles choose their leaders based on the game theory instead of a random selection. Besides, the best leader refers to other leaders' information to improve its quality in every generation based on a self-learning process. To make a comprehensive analysis, we test MLPSO against the benchmark functions in CEC 2013 and further applied MLPSO to a practical case: the reconstruction of gene regulatory networks based on fuzzy cognitive maps. The experimental results confirm that MLPSO enhances the efficiency of the canonical PSO and performs well in the realistic optimization problem.

[1]  Jing Liu,et al.  Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps , 2016, Natural Computing.

[2]  Mesut Gündüz,et al.  A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems , 2013, Appl. Soft Comput..

[3]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  Chunyan Miao,et al.  Implementation of Fuzzy Cognitive Maps Based on Fuzzy Neural Network and Application in Prediction of Time Series , 2010, IEEE Transactions on Fuzzy Systems.

[5]  Carlos A. Coello Coello,et al.  Analysis of leader selection strategies in a multi-objective Particle Swarm Optimizer , 2013, 2013 IEEE Congress on Evolutionary Computation.

[6]  Jean-Philippe Martin,et al.  A PSO-Based Global MPPT Technique for Distributed PV Power Generation , 2015, IEEE Transactions on Industrial Electronics.

[7]  Sanming Zhou,et al.  Fuzzy causal networks: general model, inference, and convergence , 2006, IEEE Transactions on Fuzzy Systems.

[8]  Athanasios V. Vasilakos,et al.  Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data , 2016, IEEE Transactions on Services Computing.

[9]  Xiaodong Li,et al.  Cooperatively Coevolving Particle Swarms for Large Scale Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[10]  Mario Marchesoni,et al.  PSO-Based Self-Commissioning of Electrical Motor Drives , 2015, IEEE Transactions on Industrial Electronics.

[11]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[12]  Lawrence J. Mazlack,et al.  Inferring Fuzzy Cognitive Map models for Gene Regulatory Networks from gene expression data , 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine.

[13]  Ying Lin,et al.  Particle Swarm Optimization With an Aging Leader and Challengers , 2013, IEEE Transactions on Evolutionary Computation.

[14]  Manel Guerrero Zapata,et al.  A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks , 2015, Neurocomputing.

[15]  Chukwudi Anyakoha,et al.  A review of particle swarm optimization. Part I: background and development , 2007, Natural Computing.

[16]  Yaochu Jin,et al.  A social learning particle swarm optimization algorithm for scalable optimization , 2015, Inf. Sci..

[17]  Jing Liu,et al.  A multiagent genetic algorithm for global numerical optimization , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Yudong Zhang,et al.  Feed‐forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection , 2015, Int. J. Imaging Syst. Technol..

[19]  Krzysztof Wiktorowicz,et al.  Evaluation of selected fuzzy particle swarm optimization algorithms , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[20]  Andries Petrus Engelbrecht,et al.  A self-adaptive heterogeneous pso for real-parameter optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[21]  Yaochu Jin,et al.  A Competitive Swarm Optimizer for Large Scale Optimization , 2015, IEEE Transactions on Cybernetics.

[22]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[24]  A. Rezaee Jordehi,et al.  Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems , 2015, Appl. Soft Comput..

[25]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[26]  Elpiniki I. Papageorgiou,et al.  Optimization of Fuzzy Cognitive Map Model in Clinical Radiotherapy Through Differential Evolution Algorithm , 2003 .

[27]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[28]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[29]  Chukwudi Anyakoha,et al.  A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications , 2008, Natural Computing.

[30]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[31]  Chunyan Miao,et al.  Dynamical cognitive network - an extension of fuzzy cognitive map , 2001, IEEE Trans. Fuzzy Syst..

[32]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[33]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[34]  K. K. Mishra,et al.  Dynamic-PSO: An improved particle swarm optimizer , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[35]  Ajith Abraham,et al.  Ideology algorithm: a socio-inspired optimization methodology , 2017, Neural Computing and Applications.

[36]  Bogdan Kwolek,et al.  Real-Time Multi-view Human Motion Tracking Using Particle Swarm Optimization with Resampling , 2012, AMDO.

[37]  Witold Pedrycz,et al.  Numerical and Linguistic Prediction of Time Series With the Use of Fuzzy Cognitive Maps , 2008, IEEE Transactions on Fuzzy Systems.

[38]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[39]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[40]  Andrés Iglesias,et al.  A new iterative mutually coupled hybrid GA-PSO approach for curve fitting in manufacturing , 2013, Appl. Soft Comput..

[41]  M. Furkan Dodurka,et al.  Goal-oriented decision support using Big Bang-Big Crunch learning based Fuzzy Cognitive Map: An ERP management case study , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[42]  Alireza Alfi,et al.  Intelligent identification and control using improved fuzzy particle swarm optimization , 2011, Expert Syst. Appl..

[43]  Liang Lv,et al.  An Adaptive Convergence Speed Controller Framework for Particle Swarm Optimization Variantsin Single Objective Optimization Problems , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[44]  Samia Nefti-Meziani,et al.  A Comprehensive Review of Swarm Optimization Algorithms , 2015, PloS one.

[45]  Witold Pedrycz,et al.  A divide and conquer method for learning large Fuzzy Cognitive Maps , 2010, Fuzzy Sets Syst..

[46]  Witold Pedrycz,et al.  Genetic learning of fuzzy cognitive maps , 2005, Fuzzy Sets Syst..

[47]  Edy Tonnizam Mohamad,et al.  Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach , 2015, Bulletin of Engineering Geology and the Environment.