The fully informed particle swarm: simpler, maybe better

The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We, thus, decided to make the individuals "fully informed." The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions.

[1]  William H. Press,et al.  Numerical Recipes in C, 2nd Edition , 1992 .

[2]  T. M. English.  Optimization is easy and learning is hard in the typical function , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[3]  A. Turing.  On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .

[4]  Robert G. Reynolds,et al.  Knowledge-based self-adaptation in evolutionary programming using cultural algorithms , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[5]  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).

[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]  Vladimiro Miranda,et al.  EPSO - best-of-two-worlds meta-heuristic applied to power system problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  T. Cook,et al.  Quasi-experimentation: Design & analysis issues for field settings , 1979 .

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

[10]  D. Wolpert,et al.  No Free Lunch Theorems for Search , 1995 .

[11]  William H. Press,et al.  Numerical recipes in C , 2002 .

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

[13]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..