【摘要】 为了解决目前基于分解的多模态多目标优化算法存在种群搜索能力不足，子种群中存在无用解和距离度量不具有普适性等问题，提出了一种基于自适应搜索的多模态多目标优化算法MOEA/D-AS.首先，该方法通过减少平均子种群的个体数量，进而增加参考向量的数量.其次，根据子种群当前状态自适应分配子种群的个体数量.最后，使用引入了局部种群信息的清除距离作为维护子种群的依据.将提出的算法与4种算法在2019年CEC多模态多目标测试问题和大规模多模态多目标测试问题上进行对比实验，实验结果表明，提出的算法可以有效解决多模态多目标优化问题.更多还原

【Abstract】 The current decomposition-based multi-modal multi-objective optimization algorithms have insufficient population search capability, useless solutions in sub-populations, and a non-universal distance metric. To address these issues, an adaptive search multi-modal multi-objective optimization algorithm MOEA/D-AS is proposed. Firstly, this method increases the number of reference vectors by reducing the size of the average sub-population. Secondly, the sub-populations are reallocated according to the current state of the sub-populations in the iteration. Finally, a clear distance based on local population information is introduced as the basis for modifying the sub-populations. The proposed algorithm is compared with four algorithms on the 2019 CEC multi-modal multi-objective test problems and the large-scale multi-modal multi-objective test problems for experiments. The experimental results show that the proposed algorithm can effectively solve the multi-modal multi-objective optimization problems.更多还原