WoSP: a multi-optima particle swarm algorithm

When investigating multi-optima problems, a particle swarm algorithm should not converge on single optima but ideally should explore many optima by continual searching. The common practice of only evaluating each particle's performance at discrete intervals can, at small computational cost, be used to adjust particle behaviour in situations where the swarm is `settling' so as to encourage the swarm to explore further. An algorithm is proposed that, by making each wave of particles partially independent, is suitable for multi-optima problems

[1]  Xiaodong Li,et al.  A particle swarm model for tracking multiple peaks in a dynamic environment using speciation , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[2]  Tim Hendtlass,et al.  Discrete evaluation and the particle swarm algorithm , 2004 .

[3]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[4]  Russell C. Eberhart,et al.  The particle swarm: social adaptation in information-processing systems , 1999 .

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

[6]  Tim Hendtlass,et al.  A Combined Swarm Differential Evolution Algorithm for Optimization Problems , 2001, IEA/AIE.

[7]  Frans van den Bergh,et al.  A NICHING PARTICLE SWARM OPTIMIZER , 2002 .