Particle Swarm Optimization with Novel Processing Strategy and Its Application

The loss of population diversity is one of main reasons which lead standard particle swarm optimization (SPSO) to suffer from the premature convergence when solving complex multimodal problems. In SPSO, the personal experience and sharing experience are processed with a completely random strategy. It is still an unsolved problem whether the completely random processing strategy is good for maintaining the population diversity. To study this problem, this paper presents a correlation PSO model in which a novel correlative strategy is used to process the personal experience and sharing experience. The relational expression between the correlation coefficient and population diversity is developed through theoretical analysis. It is found that the processing strategy with positive linear correlation is helpful to maintain the population diversity. Then a positive linear correlation PSO, PLCPSO, is proposed, where particles adopt the positive linear correlation strategy to process the personal experience and s...

[1]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[2]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[3]  Kay Chen Tan,et al.  A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Andries Petrus Engelbrecht,et al.  Measuring exploration/exploitation in particle swarms using swarm diversity , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[5]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

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

[7]  S. Kamal Chaharsooghi,et al.  An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP) , 2008, Appl. Math. Comput..

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

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

[10]  Zhao Xinchao.  A perturbed particle swarm algorithm for numerical optimization , 2010 .

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

[12]  Xiao-Feng Xie,et al.  DEPSO: hybrid particle swarm with differential evolution operator , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[13]  Yanbing Liu,et al.  A Novel Fast Multi-objective Evolutionary Algorithm for QoS Multicast Routing in MANET , 2009, Int. J. Comput. Intell. Syst..

[14]  Vishnu Prasad,et al.  Economic dispatch using particle swarm optimization: A review , 2009 .

[15]  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).

[16]  Sanghamitra Bandyopadhyay,et al.  Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..

[17]  Manoj Kumar Tiwari,et al.  Interactive Particle Swarm: A Pareto-Adaptive Metaheuristic to Multiobjective Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[18]  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).

[19]  Xiao-Feng Xie,et al.  Optimizing semiconductor devices by self-organizing particle swarm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[20]  Lifeng Xi,et al.  Evolving artificial neural networks using an improved PSO and DPSO , 2008, Neurocomputing.

[21]  Russell C. Eberhart,et al.  Population diversity of particle swarms , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[22]  Chongzhao Han,et al.  Knowledge-based cooperative particle swarm optimization , 2008, Appl. Math. Comput..

[23]  Riccardo Poli,et al.  Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms , 2006, IEEE Transactions on Evolutionary Computation.

[24]  Suganthan.  [IEEE 1999. Congress on Evolutionary Computation-CEC99 - Washington, DC, USA (6-9 July 1999)] Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) - Particle swarm optimiser with neighbourhood operator , 1999 .

[25]  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).

[26]  Amitava Chatterjee,et al.  A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding , 2008, Expert Syst. Appl..

[27]  J. Rice.  Mathematical Statistics and Data Analysis , 1988 .

[28]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..