基于自适应邻域策略的改进型MOEA/D算法

Improved MOEA/D Algorithm Based on Adaptive Neighborhood Strategy

为避免传统MOEA/D算法使用固定领域规模易造成种群进化效率降低的情况,提出一种基于自适应邻域策略的改进算法。设计一种能够反映子问题进化幅度和种群进化状态的判断机制。针对进化过程中的收敛性和分布性需求,提出基于进化状态判断的自适应邻域策略,从而根据种群和子问题的进化状态设定不同的邻域规模。使用WFG系列测试函数进行实验,结果表明,该算法能有效平衡进化过程中种群的收敛性与分布性,提高解集的整体性能。

Traditional Multi-objective Evolutionary Algorithm based on Decomposition(MOEA/D)uses the fixed neighborhood scale,which reduces the population evolution efficiency.To solve this problem,an improved algorithm based on the Adaptive Neighborhood Strategy(ANS) is proposed.This paper designs a kind of judgment mechanism which can reflect the evolution magnitude of the sub-problems and the evolutionary state of the population.Based on evolutionary state judgments,an ANS is proposed to address the requirements of convergence and distribution in the evolutionary process,and then it can set different neighborhood sizes according to the evolutionary states of the population and sub-problems.Use the WFG series of functions to do the test,and the results show that this algorithm can effectively balance the convergence and distribution of population in the evolution the process,and improve the overall performance of the solution set.