Incremental Particle Swarm Optimization

Abstract By simulating the population size of the human evolution, a PSO algorithm with increment of particle size (IPPSO) was proposed. Without changing the PSO operations, IPPSO can obtain better solutions with less time cost by modifying the structure of traditional PSO. Experimental results show that IPPSO using logistic model is more efficient and requires less computation time than using linear function in solving more complex program problems.

[1]  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).

[2]  Mehmet Çunkas,et al.  A COMPARATIVE STUDY ON PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHMS FOR TRAVELING SALESMAN PROBLEMS , 2009, Cybern. Syst..

[3]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

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

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

[6]  Konstantinos E. Parsopoulos,et al.  PARTICLE SWARM OPTIMIZER IN NOISY AND CONTINUOUSLY CHANGING ENVIRONMENTS , 2001 .

[7]  A. Groenwold,et al.  Comparison of linear and classical velocity update rules in particle swarm optimization: notes on diversity , 2007 .

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

[9]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).