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

协同演化是解决大尺度连续优化问题的一种有效策略.但是,对于决策变量重叠型(决策变量不可分且相互依赖)的高维问题,其分组方法可能会误导算法的搜索.针对这一情况,本文提出一种全新的协同演化策略(Differential Evolution Cooperative Coevolution with Correlation Learning Between Variables,DECC-CLV),其思想是首先计算演化种群分布所包含的主特征轴,然后计算各维决策变量在主轴上的投影值并利用它们之间的正负相关性进行分组.该算法在迭代过程中,利用期望最大化算法对种群进行概率主成分分析,并根据决策变量在当前种群主轴上的投影值大小关系对其进行动态分组.通过和目前主流的演化算法在CEC2013的第三类函数上的仿真试验和分析,验证了算法的有效性和适用性.

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.