A multi-swarm particle swarm optimization with local search on multi-robot search system

摘要

This paper proposes a method based on the Multi-Swarm Particle Swarm Optimization (PSO) with Local Search on the multi-robot search system to find a given target in a Complex environment that contains static obstacles. This method by applying Multi-Swarm with Multi-Best particles on the multi-robot system can overcome the premature convergence problem, which is one of the main problems of Basic PSO. As the time progress the global searching of the algorithm decrease and therefore the robots tend to get group together in the small-explored region that called Premature Convergence and cannot reach the target. By combining the Local Search method with Multi-Swarm, We can guarantee the global convergence of this proposed algorithm and the robots can reach the target. The Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.