Abstract
Recently, more and more researches have been conducted on the multi-robot system by applying bio- inspired algorithms. Particle Swarm Optimization (PSO) is one of the optimization algorithms that model a set of solutions as a swarm of particles that spread in the search space. This algorithm has solved many optimization problems, but has a defect when it is applied on search tasking. As the time progress, the global searching of PSO decreased and it converged on a small region and cannot search the other region, which is causing the premature convergence problem. In this study we have presented a simulated multi-robot search system to overcome the premature convergence problem. Experimental results show that the proposed algorithm has better performance rather than the basic PSO algorithm on the searching task.
Highlights
The results of the analysis of the social characteristics of insects and animals have a strong influence in multi-robot system research
One of the most important algorithms in this domain is Particle Swarm Optimization (PSO) (Eberhart and Kennedy, 1995), which is based on the population stochastic optimization technique that was inspired by social behavior of bird flocking and fish schooling
Couceiro et al (2011) proposed new method based on the Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO) named RPSO and RDPSO
Summary
The results of the analysis of the social characteristics of insects and animals have a strong influence in multi-robot system research. PSO algorithm like most of the stochastic search techniques suffers from the Premature Convergence problem This problem on the multi-robot search system is more significant when the fitness function is limited and each particle just can sense the limited search space around it-self. It means, as the time increase if the particles cannot sense the target in the first iterations the particles converge to the small regions because of decreasing global searching. Couceiro et al (2011) proposed new method based on the Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO) named RPSO and RDPSO This method is adapted to the multi-robot search systems that take into account the obstacle avoidance. Corresponding Author: Mohammad Naim Rastgoo, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia
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