[1]欧阳聪,关静,杨鸣.基于资源分配和动态分组的合作协同演化算法[J].郑州大学学报(工学版),2023,44(05):10-16.[doi:10.13705/j.issn.1671-6833.2023.05.010]
　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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基于资源分配和动态分组的合作协同演化算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年05期
页码:
10-16
栏目:
出版日期:
2023-08-20

文章信息/Info

Title:
Cooperative Co-evolution Algorithm Based on Resource Allocation and Dynamic Grouping
作者:
欧阳聪1; 关静2; 杨鸣1
1. 中国地质大学(武汉) 计算机学院,湖北 武汉 430078;2. 中国舰船研究设计中心,湖北 武汉 430064
Author(s):
OUYANG Cong1;  GUAN Jing2;  YANG Ming1
关键词:
合作型协同演化;  大规模全局优化;  资源分配;  动态分组;  贡献值
Keywords:
cooperative co-evolution;  large scale global optimization;  resource allocation;  dynamic grouping;  con; tributio
分类号:
TP301. 6
DOI:
10.13705/j.issn.1671-6833.2023.05.010
文献标志码:
A
摘要:
合作型协同演化算法在处理大规模全局优化问题中的决策变量完全可分或者完全不可分的问题时,精确 的分组方法并不能保证提高算法性能,甚至可能会导致性能下降。 针对上述问题,提出了一种基于资源分配和动 态分组的合作协同演化算法(RG-CCFR3) 。 该算法以 CCFR3 为基础,当决策变量完全可分或完全不可分时,首先 设置数组与数组索引,用于确定每轮优化时的分组大小;其次,根据分组大小对决策变量进行随机分组,使得在不 同轮次的优化中每组决策变量的分配更多样化;最后,修改了 CCFR3 中每轮优化时的处理逻辑,保证了每轮优化 的次数一致。 通过 CEC2013 和 CEC2010 中的基准测试函数检验算法的性能,将 RG-CCFR3 与 CCFR3、MMO-CC、 CBCC-RDG3 进行对比并进行显著性检验。 结果表明:对比 CCFR3 算法,RG-CCFR3 算法在处理决策变量完全可分 或者完全不可分的问题时,在多数情况下具有更好的性能;与 MMO-CC、CBCC-RDG3 算法相比,RG-CCFR3 算法具 有一定的竞争力。
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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更新日期/Last Update: 2023-09-03