Modified Particle Swarm Optimization for sphere, Rastrigin, Schwefel and Schafer fuctions

Particle Swarm Optimization (PSO), proposed by J. Kennedy and R. Eberhart in 1995, attracts many attentions to solve for a lot of optimization problems nowadays. Due to its simplicity of parameter-setting and computational efficiency, it becomes one of the most popular algorithms for optimizations. For different optimization problems, different settings are required in order to solve problems with saving-time and efficiency. Four most popular problems are solved in this paper while modified PSO algorithms with different parameters are set.

[1]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[2]  A. Engelbrecht.  Computational Intelligence: An Introduction, Second Edition , 2007 .

[3]  Hassan M. Elkamchouchi,et al.  Application of Particle Swarm Optimization Algorithm in Smart Antenna Array Systems , 2009 .

[4]  Omar Hegazy,et al.  Swarm Intelligence Applications in Electric Machines , 2009 .

[5]  PenChen Chou,et al.  Enforced Mutation to Enhancing the Capability of Particle Swarm Optimization Algorithms , 2011, ICSI.

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

[7]  Zhihua Cui,et al.  Individual Parameter Selection Strategy for Particle Swarm Optimization , 2009 .

[8]  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).