具有多形态种群协同进化的多目标优化算法

摘要

为提高进化多目标优化算法在维持最优解多样性方面的性能，获得分布更均匀的Pareto非支配解集，文中提出一种具有多形态种群协同进化的多目标优化算法。该算法构建一种多形态种群协同进化架构，通过引入最小向量夹角的相似性度量方法，给出次优非支配个体选择策略，从而提高种群的多样性。算法还提出一种基于排序链表的拥挤个体删除策略，进一步提高解集分布的均匀性和宽广性。与经典算法对比结果表明，文中算法在解的分布性和多样性方面均有较好表现，尤其在解集分布均匀性方面优势较明显。%To improve the diversity maintenance ability of evolutionary multi_objective optimization algorithms and obtain a set of better distributed non_dominated solutions, a co_evolutionary multi_objective optimization algorithm with polymorphous populations is proposed. Firstly, a co_evolutionary frame of polymorphous populations is designed. Next, by introducing the minimum vectorial angle which is capable of measuring the similarity between different Pareto_ranked solutions, a selection strategy for suboptimum non_dominated solutions is proposed to enhance the diversity of populations. Finally, a population removal strategy based on an ordered link_list is put forward. Thus, the uniformity and the spread of the solutions are improved. Compared with some typical algorithms, the proposed algorithm has good convergence and remains a better diversity and uniformity.