SCGSA: A sine chaotic gravitational search algorithm for continuous optimization problems

作者：

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.

摘要

•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.

论文关键词：Gravitational search algorithm,Chaotic maps,Sine cosine algorithm,Continuous optimization problem

论文评审过程：Received 29 July 2019, Revised 30 November 2019, Accepted 2 December 2019, Available online 5 December 2019, Version of Record 12 December 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113118