电子学报2018，Vol.46Issue(3)：529-536,8.DOI:10.3969/j.issn.0372-2112.2018.03.003
求解变量重叠型大尺度优化问题的相关性学习协同演化策略
Cooperative Coevolution with Correlation Learning Between Variables for Large Scale Overlapping Problem
摘要
Abstract
Cooperative co-evolution (CC) is an effective strategy to solve large-scale continuous optimization problem. However, its grouping method may mislead the search direction when solving the large-scale overlapping problem (decision variables are non-separable and interact with each other). In order to overcome this issue, we propose a differential evolution cooperative coevolution with correlation learning between variables (DECC-CLV) to improve the performance of CC. DECC-CLV firstly detects the positive and negative correlations of variables based on the projected value of decision variables on the principal component of the population, and then groups variables into different groups. During the evolutionary process, DECC-CLV employs the expectation maximization algorithm for probabilistic principal component analysis on the population to deduce the complexity. Comparing with the state-of-the-art CCs on the large-scale overlapping benchmark functions on CEC2013, the experimental results verified the effectiveness and applicability of our proposed algorithm.关键词
大尺度优化问题/相关性决策变量/协同演化/大尺度优化问题分解Key words
large-scale optimization problem/variables correlation/cooperative co-evolution/large-scale optimization problem decomposition分类
信息技术与安全科学引用本文复制引用
王豫峰,董文永,董学士..求解变量重叠型大尺度优化问题的相关性学习协同演化策略[J].电子学报,2018,46(3):529-536,8.基金项目
国家自然科学基金(No.61170305,No.61672024) （No.61170305,No.61672024）
河南省高等学校重点科研项目计划(No.17A520046) （No.17A520046）
