具有多形态种群协同进化的多目标优化算法

Co-evolutionary Multi-objective Optimization Algorithm with Polymorphous Populations

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.