An improved PSO algorithm with a territorial diversity-preserving scheme and enhanced exploration-exploitation balance

Abstract Despite its relatively high convergence rate, the particle swarm optimization (PSO) algorithm is quite vulnerable to premature convergence to local minima. To tackle this problem an improved territorial particle swarm optimization (TPSO) algorithm is presented in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. A new “collision operator” and adaptively varying “territories” are used to prevent the particles from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric, and thus improves exploration ability. The collision operator is shown to provide the algorithm with the ability of controlling the diversity throughout the different stages of the search process. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors' best found positions instead of their own personal bests which in turn helps the particles to exploit the candidate local optima more effectively and thus provides the algorithm with a local search ability. The efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly-composed benchmark functions presented in CEC2005 competition and the results are compared with recent variants of the original PSO and CMA-ES the winner of CEC2005 competition.

[1]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[2]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search , 2005, 2005 IEEE Congress on Evolutionary Computation.

[3]  Ponnuthurai N. Suganthan,et al.  Diversity enhanced particle swarm optimizer for global optimization of multimodal problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

[4]  Riccardo Poli,et al.  Particle Swarms: The Second Decade , 2008 .

[5]  Riccardo Poli,et al.  Analysis of the publications on the applications of particle swarm optimisation , 2008 .

[6]  Marco Gaviano,et al.  Test Functions with Variable Attraction Regions for Global Optimization Problems , 1998, J. Glob. Optim..

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

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

[9]  P. N. Suganthan,et al.  A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization , 2012, Inf. Sci..

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

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

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

[13]  S. N. Deepa,et al.  Model order formulation of a multivariable discrete system using a modified particle swarm optimization approach , 2011, Swarm Evol. Comput..

[14]  Millie Pant,et al.  A Simple Diversity Guided Particle Swarm Optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[15]  Masoud Shariat Panahi,et al.  On the limitations of classical benchmark functions for evaluating robustness of evolutionary algorithms , 2010, Appl. Math. Comput..

[16]  M. Ben Ghalia,et al.  Particle swarm optimization with an improved exploration-exploitation balance , 2008 .

[17]  Dilip Kumar Pratihar,et al.  Tuning of neural networks using particle swarm optimization to model MIG welding process , 2011, Swarm Evol. Comput..

[18]  Dongsheng Xu,et al.  An Improved Diversity Guided Particle Swarm Optimization , 2009, ISNN.

[20]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[21]  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).

[22]  Konstantinos E. Parsopoulos,et al.  UPSO: A Unified Particle Swarm Optimization Scheme , 2019, International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004).

[23]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[24]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[25]  Christian Posthoff,et al.  Neighborhood Re-structuring in Particle Swarm Optimization , 2005, Australian Conference on Artificial Intelligence.

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

[27]  Hui Sun,et al.  A two Sub-swarm Exchange Particle Swarm Optimization considering exploration and exploitation , 2010, 2010 The 2nd International Conference on Industrial Mechatronics and Automation.

[28]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[29]  Mehmet Fatih Tasgetiren,et al.  Dynamic multi-swarm particle swarm optimizer with harmony search , 2011, Expert Syst. Appl..

[30]  Witold Pedrycz,et al.  A new PSO-optimized geometry of spatial and spatio-temporal scan statistics for disease outbreak detection , 2012, Swarm and Evolutionary Computation.

[31]  T. Krink,et al.  Extending particle swarm optimisers with self-organized criticality , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

[33]  Martin Middendorf,et al.  A hierarchical particle swarm optimizer and its adaptive variant , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[34]  Xiao-Feng Xie,et al.  Hybrid particle swarm optimizer with mass extinction , 2002, IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions.

[35]  Sanjib Ganguly,et al.  Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization , 2012, Swarm Evol. Comput..

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

[37]  Ender Özcan,et al.  Particle Swarms for Multimodal Optimization , 2007, ICANNGA.

[38]  Kanya Tanaka,et al.  Control-theoretic analysis of exploitation and exploration of the PSO algorithm , 2010, 2010 IEEE International Symposium on Computer-Aided Control System Design.

[39]  Kalyan Veeramachaneni,et al.  Fitness-distance-ratio based particle swarm optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[40]  Nitin V. Afzulpurkar,et al.  Optimization of tile manufacturing process using particle swarm optimization , 2011, Swarm Evol. Comput..