Cooperative Co-evolution Algorithm Based on Resource Allocation and Dynamic Grouping

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Cooperative Co-evolution Algorithm Based on Resource Allocation and Dynamic Grouping

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[1]OUYANG Cong,GUAN Jing,YANG Ming.Cooperative Co-evolution Algorithm Based on Resource Allocation and Dynamic Grouping[J].Journal of Zhengzhou University (Engineering Science),2023,44(05):10-16.[doi:10.13705/j.issn.1671-6833.2023.05.010]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 05 Page number: 10-16 Column: Public date: 2023-08-20

Title:: Cooperative Co-evolution Algorithm Based on Resource Allocation and Dynamic Grouping

Author(s):: OUYANG Cong¹; GUAN Jing²; YANG Ming¹

Keywords:: cooperative co-evolution; large scale global optimization; resource allocation; dynamic grouping; con; tributio

CLC:: TP301. 6

DOI:: 10.13705/j.issn.1671-6833.2023.05.010

Abstract:: The precise grouping method might not constantly improve the algorithm performance and sometimes even lead to performance degradation when the cooperative co-evolutionary algorithm was used to solve large-scale global optimization problems with entirely separable or fully non-separable decision variables. A cooperative co-evolutionary algorithm (RG-CCFR3) based on resource allocation and dynamic grouping was proposed to address the above problems. The algorithm was based on CCFR3, where the array with the array index was first set for determining the group size at each round of optimization when the decision variables were fully divisible or fully indivisible. Secondly, the decision variables were randomly grouped according to the group size, which made the assignment of each group of decision variables more diverse in different rounds of optimization. Finally, the processing logic in CCFR3 at each round of optimization was modified to ensure a consistent number of rounds of optimization. The benchmark test functions in CEC2013 and CEC2010 were selected to examine the algorithm′s performance. And RG-CCFR3 was compared with CCFR3, MMO-CC, and CBCC-RDG3 and tested for significance. The experimental results showed that, compared with the CCFR3 algorithm, the RG-CCFR3 algorithm would perform better in most cases when dealing with problems with entirely separable or non-separable decision variables; it was also competitive with the MMO-CC and CBCC-RDG3 algorithms.

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