Repulsive Particle Swarm Optimization based on new diversity

To avoid the problem of premature convergence, a new diversity-guided Particle Swarm Optimizer (PSO), namely MARPSO is proposed, which is a modification of attractive and repulsive PSO (ARPSO), suggested by Riget and Vesterstorm[1]. A novel measure of population diversity function is presented and a new concept of the particle's best flight direction is introduced. The simulation test results of four classic functions show that: compared with Standard PSO (BPSO) and ARPSO, MARPSO can effectively increase the diversity of swarm, while maintain a higher convergence speed.

[1]  Gabriela Ciuprina,et al.  Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans Mag , 2002 .

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

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

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

[5]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[6]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[7]  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).

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

[9]  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).

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