A Survey on Cooperative Co-Evolutionary Algorithms

• Published in: IEEE transactions on evolutionary computation Vol. 23; no. 3; pp. 421 - 441
• Main Authors: Ma, Xiaoliang, Li, Xiaodong, Zhang, Qingfu, Tang, Ke, Liang, Zhengping, Xie, Weixin, Zhu, Zexuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Benchmark testing; Computer science; Cooperative co-evolutionary algorithm (CCEA); Decomposition; evolutionary algorithm (EA); Evolutionary algorithms; Fitness; genetic algorithm (GA); Genetic algorithms; Google; Optimization; Perturbation methods; Resource allocation; Resource management
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2018.2868770

The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong in 1994 and since then many CCEAs have been proposed and successfully applied to solving various complex optimization problems. In applying CCEAs, the complex optimization problem is decomposed into multiple subproblems, and each subproblem is solved with a separate subpopulation, evolved by an individual evolutionary algorithm (EA). Through cooperative co-evolution of multiple EA subpopulations, a complete problem solution is acquired by assembling the representative members from each subpopulation. The underlying divide-and-conquer and collaboration mechanisms enable CCEAs to tackle complex optimization problems efficiently, and hence CCEAs have been attracting wide attention in the EA community. This paper presents a comprehensive survey of these CCEAs, covering problem decomposition, collaborator selection, individual fitness evaluation, subproblem resource allocation, implementations, benchmark test problems, control parameters, theoretical analyses, and applications. The unsolved challenges and potential directions for their solutions are discussed.