SCGSA: A sine chaotic gravitational search algorithm for continuous optimization problems. (15th April 2020)

• Record Type: Journal Article
• Authors: Jiang, Jianhua; Jiang, Ran; Meng, Xianqiu; Li, Keqin
• Is Part Of: Expert systems with applications. Volume 144(2020)
• Publication Date: 2020-04-15
• Subjects: Gravitational search algorithm -- Chaotic maps -- Sine cosine algorithm -- Continuous optimization problem; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.113118
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: SCGSA is inspired by SCA(sine cosine algorithm). Sine moving pattern and k are designed to balance exploration and exploitation. SCGSA solves the problem that CGSA is prone to suffer from local optima. SCGSA performs well in high dimension. The result is superior to various well-known algorithms in optimization domain. Abstract: Gravitational search algorithm (GSA), as one of the novel meta-heuristic optimization algorithms inspired by the law of gravity and mass interactions, is however prone to local optima stagnation due to heavier gravity. Hence, an enhanced version, chaotic gravitational constants for the gravitational search algorithm (CGSA), was proposed to improve the exploration ability through various chaotic maps. In this paper, with insightful utilization of sine cosine algorithm, we put forward sine chaotic gravitational search algorithm (SCGSA) as a further step of CGSA to escape from its local optima stagnation. The experiments show remarkable results in both the speed of convergence and the ability of finding global optima in 30 benchmark functions (CEC 2014), thus proving a better balance between exploration and exploitation in SCGSA compared with CGSA.