Particle Swarm Optimization with Local Search

In this paper, we propose a hybrid algorithm of particle swarm optimization and local search (PSO-LS). In PSO-LS, each particle has a chance of self-improvement by applying local search algorithm before it communicates information with other particles in the swarm. Then we modify our basic PSO-LS by choosing specific good particles as initial solutions for local search. The comparative experiments were made between PSO-LS, modified PSO-LS and PSO with linearly decreasing inertia weight (PSO-LDW) on three benchmark functions. Results show hybrid algorithms of combining particle swarm optimization with local search techniques outperform PSO-LDW