CAJ | 학술논문

　　为了改善粒子群多目标优化算法的分布性，引入了聚集密度以进行精英集的更新。其基本思想为：计算群体中每个个体的聚集密度，根据目标函数值和聚集密度定义一个偏序集，采用比例选择原则依次从偏序集中选择个体，更新精英集。通过数值实验用量化指标研究了新算法的收敛性和分布性，结果表明：新算法的收敛性与常规粒子群多目标优化算法相当，但分布性有了明显的提高。
　　위료개선입자군다목표우화산법적분포성，인입료취집밀도이진행정영집적경신。기기본사상위：계산군체중매개개체적취집밀도，근거목표함수치화취집밀도정의일개편서집，채용비례선택원칙의차종편서집중선택개체，경신정영집。통과수치실험용양화지표연구료신산법적수렴성화분포성，결과표명：신산법적수렴성여상규입자군다목표우화산법상당，단분포성유료명현적제고。
In order to improve the distribution of multi-objective PSO algorithm, crowding-density is introduced for the update of elite set. The basic idea is:the crowding-density of each individual in the group is calculated, and then a partial order set is set up according to the objective function value and crowding-density. Individuals are selected from the partial order set according to the principle of proportional selection, and the elite set is updated. The convergence and distribution of improved algorithm are studied by means of numerical experiments, and results show that the convergence of improved algorithm is roughly equal with the conventional multi-objective particle swarm optimization algorithm, but the distribution of improved algorithm has been significantly improved.