Title: Improved gravitational search algorithm based on chaotic local search

Authors: Zhaolu Guo; Wensheng Zhang; Shenwen Wang

Addresses: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China; Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China ' Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China ' School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China

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.

Keywords: evolutionary algorithm; optimisation algorithm; gravitational search; local search; chaotic map.

DOI: 10.1504/IJBIC.2021.114873

International Journal of Bio-Inspired Computation, 2021 Vol.17 No.3, pp.154 - 164

Received: 12 Apr 2020
Accepted: 02 Jul 2020
Published online: 10 May 2021 *

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