Power Mutation Embedded Modified PSO for Global Optimization Problems

In the present study we propose a simple and modified framework for Particle Swarm Optimization (PSO) algorithm by incorporating in it a newly defined operator based on Power Mutation (PM). The resulting PSO variants are named as (Modified Power Mutation PSO) MPMPSO and MPMPSO 1 which differs from each other in the manner of implementation of mutation operator. In MPMPSO, PM is applied stochastically in conjugation with basic position update equation of PSO and in MPMPSO 1, PM is applied on the worst particle of swarm at each iteration. A suite of ten standard benchmark problems is employed to evaluate the performance of the proposed variations. Experimental results show that the proposed MPMPSO outperforms the existing method on most of the test functions in terms of convergence and solution quality.

[1]  Xiaoling Wu,et al.  Particle swarm optimization based on power mutation , 2009, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management.

[2]  Hitoshi Iba,et al.  Particle swarm optimization with Gaussian mutation , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

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

[4]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[5]  Kalyan Veeramachaneni,et al.  Optimization Using Particle Swarms with Near Neighbor Interactions , 2003, GECCO.

[6]  James Kennedy,et al.  Proceedings of the 1998 IEEE International Conference on Evolutionary Computation [Book Review] , 1999, IEEE Transactions on Evolutionary Computation.

[7]  A. E. Eiben,et al.  Evolutionary Programming VII , 1998, Lecture Notes in Computer Science.

[8]  Qing Zhang,et al.  Fast Multi-swarm Optimization with Cauchy Mutation and Crossover Operation , 2007, ISICA.

[9]  Kusum Deep,et al.  A new mutation operator for real coded genetic algorithms , 2007, Appl. Math. Comput..

[10]  Hui Wang,et al.  A Hybrid Particle Swarm Algorithm with Cauchy Mutation , 2007, 2007 IEEE Swarm Intelligence Symposium.

[11]  Sanyou Zeng,et al.  Advances in Computation and Intelligence, Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings , 2007, ISICA.

[12]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.

[13]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[15]  Ying Tan,et al.  Particle Swarm Optimization Using Lévy Probability Distribution , 2007, ISICA.

[16]  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).

[17]  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).

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