Improvement of particle swarm optimization based on the repetitive search guideline

Particle Swarm Optimization (PSO), which has attracted a great deal of attention as a global optimization method in recent years, has a drawback in that its continuous search based on its excellent dynamic characteristics can not be executed stably until the end of computation due to its much strong convergence trend. In this paper, we propose "Repetitive Search Guideline" which differs from a common guideline in the improved methods which have ever been proposed and by which the continuous search of PSO is achieved without lack of PSO's excellent dynamic characteristics due to the repetitive search in a promise area where objective function value is expected to be small. We consider four improved methods based on the proposed guideline, and then, their effectiveness are confirmed through applications to 100 variables multi-peaked benchmark problems.