基于构造学习的差分进化算法求解部分可分优化问题

A Constructive Learning Differential Evolution Algorithm for Partially Separable Function Optimization Problems

复杂优化问题中决策变量之间的依赖性导致函数适应度地形中存在大量的局部最优解,传统进化算法求解此类问题相对困难。提出一种求解部分可分函数优化问题的构造学习差分进化算法CLSHADE。该算法首先利用差分分组技术将复杂问题解耦划分为多个子问题,降低问题复杂程度;然后基于分组结构设计一种构造学习策略,以一定概率向构造的最优解学习以引导种群的搜索方向,提高算法搜索性能。在CEC 2017部分可分测试函数上的实验结果表明了CLSHADE的有效性。

The dependence among decision variables in complex optimization problems leads to the appearance of a large number of local optimal solutions in the fitness landscape of functions,which are difficult to be solved by classical evolutionary algorithms.In this paper,a constructive learning success-history based adaptive differential evolution(CLSHADE)algorithm is proposed to solve partially separable function optimization problems.Firstly,CLSHADE uses the differential grouping technology(DG)to reduce the complexity of a complex problem by dividing it into multiple sub-problems.Then,a constructive learning strategy,based on the grouping structure,is designed.It learns from the constructed optimal solution in a certain probability to guide the search direction and improve the search performance of the CLSHADE.The experimental results on the partially separable function of CEC 2017 demonstrate the effectiveness of the CLSHADE.