文献类型：期刊文献

英文题名：Multi-system genetic algorithm for complex system optimization

作者：Ma, Haiping[1];Shan, Yu[1];Wang, Jinglin[2];Chen, Xiaolei[3];Yang, Zhile[4];Simon, Dan[5]

机构：[1]Shaoxing Univ, Dept Elect Engn, Shaoxing, Zhejiang, Peoples R China;[2]Univ Nottingham Ningbo China, Dept Elect & Elect Engn, Ningbo, Zhejiang, Peoples R China;[3]Chongqing Univ Posts & Telecommun, Sch Automat, Chongqing, Peoples R China;[4]Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Guangdong, Peoples R China;[5]Cleveland State Univ, Dept Elect & Comp Engn, Cleveland, OH 44115 USA

年份：0

外文期刊名：SOFT COMPUTING

收录：SCI-EXPANDED(收录号：WOS:000821726900001)、、EI(收录号：20222812336451)、Scopus(收录号：2-s2.0-85133636101)、WOS

基金：This research is supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY19F030011, the National Natural Science Foundation of China under Grant Nos. 52077213 and 62003332, the National Key Research and Development Project of China under Grant No. 2018YFB1702200.

语种：英文

外文关键词：Evolutionary computation; Multi-system genetic algorithm; Complex system optimization; Multi-objective optimization; Migration

外文摘要：Complex system optimization is an emerging research topic in the field of evolutionary computation, whose goal is to handle complex systems with multiple coupled subsystems, each including multiple objectives and multiple constraints in real-world applications. This paper proposes a multi-system genetic algorithm (MSGA), stemming from implicit parallelism in population-based search algorithms, to solve multiple coupled subsystems simultaneously in a complex system. The proposed MSGA is composed of within-subsystem evolution and cross-subsystem migration operators. The objective of the former is to optimize each subsystem by appropriate search strategies, and the objective of the latter is to exchange information between multiple subsystems by migration, which is based on the similarity probability of objectives and constraints, and the intersection probability of solutions in different subsystems. During migration across subsystems, three statistical approaches of measuring similarity and three metrics of solution intersection in information theory are used to calculate these probabilities. Performance is tested on a set of multi-subsystem benchmark functions, and the simulation results show that cross-subsystem migration plays the key role for the performance of MSGA. Furthermore, the proposed MSGA is compared with other competitive algorithms, and results show that it is a promising multi-system optimization algorithm. In summary, the contribution of this paper is the introduction of multi-system optimization to the EA community.