A Self-adaptive Mechanism Embedded Gravitational Search Algorithm

Abstract

Gravitational search algorithm (GSA) has attracted more and more attention in dealing with complex optimization problems. However, it still suffers from some major drawbacks, such as poor exploitation. This paper has proposed a novel self-adaptive chaotic search mechanism embedded GSA (SA-GSA), in which different chaotic maps are utilized to guide the local search in a self-adaptive way. Specifically, instead of defining the search range randomly and arbitrarily, the distance between distinct individuals has been utilized in the current population as the search range. Thus the search range will decrease synchronously according to the convergence speed of the population, which is thought to improve the exploitation ability of GSA effectively. To evaluate the performance of SA-GSA, we compare it with classic GSA and the classic chaotic GSA (CGSA) on 23 benchmark optimization problems. The experimental results and statistic analysis verify that SA-GSA is superior to its competitors in terms of convergence speed and solution accuracy rate.