文献类型：期刊文献

英文题名：A Parallel Social Spider Optimization Algorithm Based on Emotional Learning

作者：Lai, Zhaolin[1];Feng, Xiang[1];Yu, Huiqun[1];Luo, Fei[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci, Shanghai 200237, Peoples R China

年份：2021

卷号：51

期号：2

起止页码：797

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：;EI(收录号：20185106275625);WOS:【SCI-EXPANDED(收录号:WOS:000608693000013)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61472139 and Grant 61462073, in part by the Information Development Special Funds of Shanghai Economic and Information Commission under Grant 201602008, and in part by the Open Funds of Shanghai Smart City Collaborative Innovation Center. This paper was recommended by Associate Editor G. Nicosia.

语种：英文

外文关键词：Clustering; emotional learning; parallel; social spider optimization (SSO)

摘要：Social spider optimization (SSO) is a swarm algorithm designed for solving complex optimization problems. It is an effective approach for searching a global optimum by simulating the cooperative behavior of social-spiders. However, SSO takes too much computation time and shows premature convergence on some problems. In order to accelerate the computation speed and further enhance the search ability, a parallel SSO (PSSO) algorithm with emotional learning is proposed in this paper. First, we develop a parallel structure for the female and male individuals to update their positions, and each individual can be computed in parallel during the search process. Second, an emotional learning mechanism is used to increase swarm diversity which is helpful to improve the search performance. Furthermore, the convergence property and computational complexity of PSSO are discussed in detail. To test the effectiveness of the proposed algorithm, it is applied to solve data clustering problem. The experimental results demonstrate that the overall performance of PSSO is superior to six other clustering algorithms on several standard data sets. In the aspect of search performance, the results obtained by PSSO are better than the comparison algorithms in most used data sets. In the aspect of time performance, the computation time of PSSO is greatly reduced in the parallel computing environment. It is comparable with K-means which is the fastest among the comparison algorithms when the number of processors larger than and equals to 16.