Improved gravitational search algorithm based on chaotic local search
by Zhaolu Guo; Wensheng Zhang; Shenwen Wang
International Journal of Bio-Inspired Computation (IJBIC), Vol. 17, No. 3, 2021

Abstract: The traditional gravitational search algorithm (GSA) maintains good diversity of solutions but often demonstrates weak local search ability. To promote the local search ability of GSA, a new GSA based on chaotic local search (CLSGSA) is introduced in this paper. In its search operations, CLSGSA first executes the conventional search operations of the basic GSA to maintain the diversity of solutions. After that, CLSGSA executes a chaotic local search with the search experience from the current best solution to increase the local search capability. In the experiments, we utilise a suite of benchmark functions to verify the performance of CLSGSA. Moreover, we compare the proposed CLSGSA with several GSA variants. The comparisons validate the effectiveness of CLSGSA.

Online publication date: Mon, 10-May-2021

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