一种改进的基于密度的多目标进化算法

摘要

Multi-objective evolutionary algorithm that diversifies population by its density (MODdEA)solve multi-objective optimization problem according to the non-dominated sorting information and spatial density information,the algo-rithm has a good performance in the comparison with other multi-objective evolutionary algorithm.In this paper,we propose an improved multi-objective evolutionary algorithm MODdEA +based on MODdEA.Firstly,we propose a operator named clone operator based on the partition mechanism in search space,this operator could not only improve the global search capa-bilities in the early stage of evolution,but also enhance the local refinement capabilities in the late stage of evolution;second-ly,we introduce a evaluation strategy which evaluate the individuals in Pareto information list based on the dominate and dominated information,this strategy provide a more accurate sorting resufinally,we improve the mutation operator in order to reduce the probability of overstep of the boundary.To demonstrate the effectiveness of the improved algorithm,we com-pare it with MODdEA on multiple testing problems,the experimental results show that the improved algorithm’s solving quality is much better than the original algorithm’s.%多目标密度驱动进化算法（MODdEA）利用非支配等级信息和分区密度信息求解多目标优化问题，该算法在与其他多目标进化算法的比较中有着出色的表现。在其基础上本文提出了一种改进的多目标进化算法 MODdEA＋，首先在该算法中基于搜索空间的分区机制提出了克隆操作，该操作不但能在进化前期增强算法的全局搜索能力，还能在进化后期提高算法的局部精化能力；其次引入一种基于 Pareto 信息表中个体支配及被支配信息的评价策略以使对信息表个体的排序结果更加精确；最后对变异操作进行了改进以降低出现不必要越界情况的概率。为验证改进算法的有效性，在对其进行分析的基础上针对多个测试问题将其与原算法进行了实验比较，结果表明改进算法的求解质量明显优于原算法。