4.7 Article

A decomposition approach for large-scale non-separable optimization problems

期刊

APPLIED SOFT COMPUTING
卷 115, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.asoc.2021.108168

关键词

Cooperative coevolution; Large-scale optimization; Overlapping functions

资金

  1. ARC, Australia Dis-covery Project [DP210102939]

向作者/读者索取更多资源

In this paper, a novel algorithm is proposed to minimize the number of common variables between sub-problems in large-scale optimization, which improves the performance of the optimization process.
Large-scale optimization is the key to many practical decision processes. To deal with the dimensional issue in such problems, many approaches incorporate a divide-and-conquer strategy. Among them, cooperative coevolution approaches have recently gained popularity. Depending on the problem's structure, the decomposition of any large problem, into a number of smaller sub-problems, may leave some variables common in more than one sub-problem. Such a decomposition may have a negative effect on the quality of the final solution of an optimization problem. In this paper, we have proposed an algorithm that incorporates a novel decomposition method, where the objective of decomposition is to minimize the number of common variables between sub-problems, achieved by exploiting a variable interaction matrix developed from the problem. So the algorithm works as a two-stage approach, where the first stage is the problem decomposition, and the second stage is to find the solutions of the problem. The performance of our proposed algorithm is assessed by solving different sets of large-scale non-separable benchmark functions with up to 2,905 variables. The experimental results provide important insights into the efficiency of the proposed decomposition method, which in turn improves the performance of the optimization process. (C) 2021 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据