期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 537, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.physa.2019.122621
关键词
Optimization algorithm; Gravitational search algorithm; Sine cosine algorithm; Balance adjustment mechanism
资金
- National Natural Science Foundation of China [61572225]
- Natural Science Foundation of the Science and Technology Department of Jilin Province, China [20180101044JC]
- Social Science Foundation of Jilin Province, China [2019B68, 2017BS28]
- Foundation of the Education Department of Jilin Province, China [JJKH20180465 kJ]
- Foundation of Jilin University of Finance and Economics [2018Z05]
The gravitational search algorithm (GSA) is a population-based meta-heuristic optimization algorithm which finds the optimal solution by the law of gravity and attraction between objects. However, as the number of iterations increases, the increase of the quality of the agents makes GSA fall into the local optimal solution more easily, which greatly reduces the exploration capability of the algorithm. Although the chaotic gravitational search algorithm (CGSA) uses chaotic maps for improving diversity to solve this problem, it still has problems with the balance of exploration and exploitation. This paper proposes the balance adjustment based chaotic gravitational search algorithm (BA-CGSA), which introduces the sine randomness function and the balance mechanism to solve the above problem. 30 benchmark functions of IEEE CEC 2014 are adopted to evaluate the performance of the proposed algorithm in terms of exploration and exploitation. Meanwhile, a real engineering design problem is used to illustrate the ability of the algorithm to solve practical application problems. The experimental results demonstrate its good performance in continuous optimization problems. (C) 2019 Elsevier B.V. All rights reserved.
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