基于精英重组的混合多目标进化算法

Elite-recombination-based hybrid multi-objective evolutionary algorithm

针对多目标进化算法搜索效率低和收敛性差的问题,提出了基于精英重组的混合多目标进化算法,将多目标优化问题分解为多个单目标优化问题单独求解,并采用基于遗传算法的精英重组策略将多个相异解重组生成唯一的精英解.提出区域化的种群初始化方法,改进局部搜索及群体选择机制,采用以优化子群为核心的分组交叉策略及自适应多位变异算子,并引入基于混沌优化的重启机制,有效克服了精英保存的固有缺陷,以及现有多目标进化算法存在的目标空间解拥挤、收敛慢、易早熟等问题.多目标测试函数的数值仿真和关键步骤的性能分析证明了本文算法的有效性和优越性.

Considering the bad efficiency and convergence of multi-objective evolutionary algorithms, this article introduces an elite-recombination-based hybrid multi-objective evolutionary algorithm （ERHMEA）. In the algorithm, the multi-objective optimization problem was decomposed into multiple single-objective optimization problems and generated the only elite solution with the genetic-algorithm-based elite recombination strategy. Strategies such as regional population initialization, improved local search and selection mechanisms, optimized subgroup based packet crossover and adaptive multiple mutation operator, and chaos optimization based restart mechanism effectively overcome the inherent defects of elite preservation, as well as the multi-objective evolutionary algorithm （MEA） existing target space solution crowding, slow convergence, prematurity, and other issues. Multi-objective test functions analysis and experimental simulation prove the effectiveness and superiority of the proposed algorithm.