Elite Opposition-Based Social Spider Optimization Algorithm for Global Function Optimization

Algorithms

Yongquan Zhou¹  Qifang Luo¹  Ruxin Zhao¹ 

[1] School of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China;

关键词: social spider optimization;    elite opposition-based learning;    elite opposition-based social spider optimization;    function optimization;   

DOI  :  10.3390/a10010009

来源: DOAJ

【 摘 要 】

The Social Spider Optimization algorithm (SSO) is a novel metaheuristic optimization algorithm. To enhance the convergence speed and computational accuracy of the algorithm, in this paper, an elite opposition-based Social Spider Optimization algorithm (EOSSO) is proposed; we use an elite opposition-based learning strategy to enhance the convergence speed and computational accuracy of the SSO algorithm. The 23 benchmark functions are tested, and the results show that the proposed elite opposition-based Social Spider Optimization algorithm is able to obtain an accurate solution, and it also has a fast convergence speed and a high degree of stability.

【 授权许可】

Unknown