Performance-dependent attractive and repulsive particle swarm optimisation

Attractive and repulsive particle swarm optimisation (ARPSO) is a novel variant of particle swarm optimisation algorithm aiming to enhance the exploration capability by dynamically changing the population diversity. However, its performance is not always well due to a simple uniform control manner. Therefore, in this paper, a performance-dependent attractive and repulsive particle swarm optimisation (PDARPSO) is designed to improve the performance of ARPSO by introducing the different trajectory for each particle according to its performance. Simulation results show the proposed algorithm can achieve better balance between exploration and exploitation capabilities.

[1]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[2]  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).

[3]  Douglas B. Kell,et al.  The landscape adaptive particle swarm optimizer , 2008, Appl. Soft Comput..

[4]  Jiangye Yuan,et al.  A modified particle swarm optimizer with dynamic adaptation , 2007, Appl. Math. Comput..

[5]  Ettore Francesco Bompard,et al.  A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment , 2005 .

[6]  Ying Tan,et al.  Predicted modified PSO with time-varying accelerator coefficients , 2009, Int. J. Bio Inspired Comput..

[7]  Michael R. Lyu,et al.  A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training , 2007, Appl. Math. Comput..

[8]  B. Suman,et al.  A survey of simulated annealing as a tool for single and multiobjective optimization , 2006, J. Oper. Res. Soc..

[9]  Z. Cui,et al.  A FAST PARTICLE SWARM OPTIMIZATION , 2006 .

[10]  Yi He,et al.  High Dimension Complex Functions Optimization Using Adaptive Particle Swarm Optimizer , 2006, RSKT.

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

[12]  Vigneshwaran Namasivayam,et al.  Research Article: pso@autodock: A Fast Flexible Molecular Docking Program Based on Swarm Intelligence , 2007, Chemical biology & drug design.

[13]  Kwok-Wo Wong,et al.  An improved particle swarm optimization algorithm combined with piecewise linear chaotic map , 2007, Appl. Math. Comput..

[14]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[15]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[16]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[17]  Chu Kiong Loo,et al.  Hybrid particle swarm optimization algorithm with fine tuning operators , 2009, Int. J. Bio Inspired Comput..

[18]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[19]  M. Senthil Arumugam,et al.  ON THE OPTIMAL CONTROL OF SINGLE-STAGE HYBRID MANUFACTURING SYSTEMS VIA NOVEL AND DIFFERENT VARIANTS OF PARTICLE SWARM OPTIMIZATION ALGORITHM , 2005 .

[20]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[21]  Horst F. Wedde,et al.  The wisdom of the hive applied to mobile ad-hoc networks , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[22]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[23]  Ying Tan,et al.  Dispersed particle swarm optimization , 2008, Inf. Process. Lett..

[24]  Ajith Abraham,et al.  Variable Neighborhood Particle Swarm Optimization for Multi-objective Flexible Job-Shop Scheduling Problems , 2006, SEAL.

[25]  Amitava Chatterjee,et al.  Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization , 2006, Comput. Oper. Res..

[26]  Zhang Yuling,et al.  On Some Non-linear Decreasing Inertia Weight Strategies in Particle Swarm Optimization , 2006, 2007 Chinese Control Conference.

[27]  Renbin Xiao,et al.  Two hybrid compaction algorithms for the layout optimization problem , 2007, Biosyst..

[28]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[29]  Ziya Arnavut,et al.  Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization , 2007, Parallel Comput..

[30]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[31]  Yongling Zheng,et al.  On the convergence analysis and parameter selection in particle swarm optimization , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[32]  M. Senthil Arumugam,et al.  On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems , 2008, Appl. Soft Comput..

[33]  G.K. Venayagamoorthy,et al.  Comparison of non-uniform optimal quantizer designs for speech coding with adaptive critics and particle swarm , 2005, Fourtieth IAS Annual Meeting. Conference Record of the 2005 Industry Applications Conference, 2005..

[34]  Shiyou Yang,et al.  An improved particle swarm optimization algorithm for global optimizations of electromagnetic devices , 2007 .

[35]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[36]  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.

[37]  Liyan Zhang,et al.  Empirical study of particle swarm optimizer with an increasing inertia weight , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[38]  Kevin M. Passino,et al.  Biomimicry for Optimization, Control and Automation , 2004, IEEE Transactions on Automatic Control.

[39]  Thomas Kiel Rasmussen,et al.  Hybrid Particle Swarm Optimiser with breeding and subpopulations , 2001 .

[40]  Dong Hwa Kim,et al.  A hybrid genetic algorithm and bacterial foraging approach for global optimization , 2007, Inf. Sci..

[41]  Lifeng Xi,et al.  An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks , 2007, Neural Processing Letters.

[42]  Michael N. Vrahatis,et al.  Tackling magnetoencephalography with particle swarm optimization , 2009, Int. J. Bio Inspired Comput..