期刊
INFORMATION SCIENCES
卷 635, 期 -, 页码 298-327出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2023.03.138
关键词
Exploration; Exploitation; Diversity; GSA; Chaotic behavior
The study proposes an algorithm that enhances the exploration capability of GSA by using a disruption strategy with chaotic dynamics. It has been shown to outperform GSA, CGSA, and PSO on benchmark functions and can solve practical engineering problems effectively.
The gravitational search algorithm (GSA) is one of the most promising algorithm in the physics-based metaheuristics category. However, GSA suffers from premature convergence due to rapid reduction in diversity, whereas a chaotic gravitational search algorithm (CGSA) can degrade the convergence speed and exploitation power. To address these issues, the current study proposes an algorithm that enhances the exploration capability of GSA using a disruption strategy with chaotic dynamics. If no significant change is observed in diversity values during the initial stages of the search process, disruption is performed using a sigmoid function. Then, the search process executes gradual exploitation using a sigmoid function without chaotic dynamics. The proposed algorithm is tested with GSA, CGSA, and PSO (particle swarm optimization) on 28 benchmark functions. It is observed that the algorithm outperforms GSA and PSO in 19 cases and CGSA in 20 cases. Diversity analysis shows that the algorithm generates superior exploration versus exploi-tation percentage with improved mean diversity values. To determine its robustness, the algo-rithm is applied to four unconstrained engineering problems. The results suggest that the algorithm can solve practical engineering problems in a reasonable number of iterations.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据