An Opposition-Based Chaotic Salp Swarm Algorithm for Global Optimization

被引:61
作者
Zhao, Xiaoqiang [1 ,2 ]
Yang, Fan [1 ,2 ]
Han, Yazhou [1 ,2 ]
Cui, Yanpeng [1 ,2 ]
机构
[1] Xian Univ Posts & Telecommun, Sch Commun & Informat, Xian 710121, Peoples R China
[2] Xian Univ Posts & Telecommun, Shaanxi Key Lab Informat Commun Network & Secur, Xian 710121, Peoples R China
基金
中国国家自然科学基金;
关键词
Salp swarm algorithm; global optimization; meta-heuristic algorithms; opposition-based learning; chaotic local search; STRATEGY;
D O I
10.1109/ACCESS.2020.2976101
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The salp swarm algorithm (SSA) is a bio-heuristic optimization algorithm proposed in 2017. It has been proved that SSA has competitive results compared to several other well-known meta-heuristic algorithms on various optimization problem. However, like most meta-heuristic algorithms, SSA is prone to problems such as local optimal solution and a slow convergence rate. To solve these problems, a chaotic salp swarm algorithm based on opposition-based learning (OCSSA) is proposed. The application of opposition-based learning (OBL) guarantees a better convergence speed and better develops the search space. The chaotic local search (CLS) method is also introduced, which can improve the performance of the algorithm to obtain the global optimal solution. The performance of OCSSA is compared with that of the original SSA and some other meta-heuristic algorithms on 28 benchmark functions with unimodal or multimodal characteristics. The experimental results show that the performance of OCSSA, with an appropriate chaotic map, is better than or comparable with the SSA and other meta-heuristic algorithms.
引用
收藏
页码:36485 / 36501
页数:17
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