A Three-Level Recursive Differential Grouping Method for Large-Scale Continuous Optimization
A Three-Level Recursive Differential Grouping Method for Large-Scale Continuous Optimization
复制标题
大规模连续优化的三级递归差分分组方法
DOI：
10.1109/access.2020.3013661
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Hao Shen
中科院分区：
文献类型：
--
作者：
Hong-Bin Xu;Fei Li;Hao Shen
合作协同进化（CC）被广泛用于解决大规模连续优化问题，它通过分解方法将一个大规模问题分解为几个小规模子问题，然后分别对每个子问题进行优化。然而
Cooperative co-evolution (CC) is widely used to solve large-scale continuous optimization problems, which divides a large-scale problem into several small-scale sub-problems via decomposition methods and then optimizes each sub-problem separately. However, the performance of CC mainly depends on the decomposition methods. A recently proposed bisection-based decomposition method, called recursive differential grouping (RDG), shows good performance when solving large-scale continuous optimization problems. In order to further improve the performance of RDG, this paper develops a novel decomposition method, called three-level recursive differential grouping (TRDG). In TRDG, when the interaction between two sets is detected, the variables in one of the sets are divided into three subsets based on the trichotomy method, and then the interaction between each subset and the other set is detected. Compared with RDG, TRDG can reduce the depth of recursion, thus saving the number of fitness evaluations (FEs). In addition, we devise a novel strategy to update adaptively the threshold for identifying the interactions between variables. The simulation experiment results on CEC’2010 and CEC’2013 benchmark functions show that the performance of TRDG is better than several existing decomposition methods in terms of the accuracy and the number of FEs. Furthermore, TRDG is embedded into two frameworks to tackle CEC’2010 large-scale continuous optimization problems.
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DOI：
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发表时间：
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期刊：
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影响因子：
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期刊：
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影响因子：
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发表时间：
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期刊：
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