Transition from particle swarm optimization to individual particle optimization

This paper proposes a novel optimization method in order to real time implementation based on standard particle swarm optimization idea and characteristics of chaotic maps which is named as individual particle optimization (IPO). Three typical benchmark functions are used to validate the proposed algorithm performance and runtime and then compare with that of the other algorithms known as modified PSOs. Six functions reported in SIS2005 are used to better verification of the proposed algorithm. Numerical results indicate that IPO is competitive due to its more competitive convergence rate and ability to find the functions' global optimum.

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

[2]  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.

[3]  Jing J. Liang,et al.  Novel composition test functions for numerical global optimization , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[4]  Zengqiang Chen,et al.  New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process , 2007, IEEE Transactions on Neural Networks.

[5]  Bo Liu,et al.  Improved particle swarm optimization combined with chaos , 2005 .

[6]  Jing J. Liang,et al.  Evaluation of Comprehensive Learning Particle Swarm Optimizer , 2004, ICONIP.

[7]  Xin Chen,et al.  Mobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN , 2005, ICNC.

[8]  Kevin D. Seppi,et al.  Adaptive diversity in PSO , 2006, GECCO '06.

[9]  Bing Li,et al.  Optimizing Complex Functions by Chaos Search , 1998, Cybern. Syst..

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

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

[12]  Roberto Battiti,et al.  The gregarious particle swarm optimizer (G-PSO) , 2006, GECCO '06.

[13]  Kalyanmoy Deb,et al.  A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization , 2002, Evolutionary Computation.

[14]  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).

[15]  P. Yadmellat,et al.  Fuzzy Parameter Particle Swarm Optimization , 2008, 2008 First International Conference on Intelligent Networks and Intelligent Systems.

[16]  Jacques Riget,et al.  A Diversity-Guided Particle Swarm Optimizer - the ARPSO , 2002 .

[17]  Wang Ling.  Survey on Chaotic Optimization Methods , 2001 .

[18]  A. Stacey,et al.  Particle swarm optimization with mutation , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[19]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[21]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[22]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[23]  Wenjun Zhang,et al.  Dissipative particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[24]  Colin G. Johnson,et al.  Varying the Topology and the Probability of Re-initialization in Dissipative Particle Swarm Optimisation , 2005 .

[25]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[26]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[27]  Nakamura Yuichi,et al.  Approximation of Dynamical Systems by Continuous Time Recurrent Neural Networks -- Extension to Non-autonomous Systems, and Approximate Extended Conditions , 2009 .

[28]  A.A. Abido,et al.  Particle swarm optimization for multimachine power system stabilizer design , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[29]  Peyman Yadmellat,et al.  A Hybrid Algorithm for Training Recurrent Fuzzy Neural Network , 2008, Artificial Intelligence and Pattern Recognition.

[30]  Hirotaka Yoshida,et al.  A PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER AND VOLTAGE CONTROL CONSIDERING VOLTAGE STABILITY , 2000 .

[31]  A.P. Engelbrecht,et al.  Learning to play games using a PSO-based competitive learning approach , 2004, IEEE Transactions on Evolutionary Computation.

[32]  T. Krink,et al.  Particle swarm optimisation with spatial particle extension , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[33]  Yu Wang,et al.  Adaptive Inertia Weight Particle Swarm Optimization , 2006, ICAISC.

[34]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[35]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.