Spherical evolution for solving continuous optimization problems

被引:72
作者
Tang, Deyu [1 ,2 ,3 ]
机构
[1] Guangdong Pharmaceut Univ, Sch Med Informat & Engn, Guangzhou 510006, Guangdong, Peoples R China
[2] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China
[3] Guangdong Prov Precise Med Big Data Tradit Chines, Guangzhou 510006, Guangdong, Peoples R China
关键词
Differential evolution; Spherical evolution; Search pattern; Spherical search style; Data clustering optimization; META-HEURISTIC OPTIMIZATION; KRILL HERD; ALGORITHM; SEARCH;
D O I
10.1016/j.asoc.2019.105499
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In these years, more and more nature-inspired meta-heuristic algorithms have been proposed; search operators have been their core problem. The common characteristics or mechanism of search operators in different algorithms have not been represented by a standard format. In this paper, we first propose the concept of a search pattern and a search style represented by a mathematical model. Second, we propose a new search style, namely a spherical search style, inspired by the traditional hypercube search style. Furthermore, a spherical evolution algorithm is proposed based on the search pattern and spherical search style. At the end, 30 benchmark functions of CEC2017 and a real-world optimization problem are tested. Experimental results and analysis demonstrate that the proposed method consistently outperforms other state-of-the-art algorithms. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页数:20
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