CAJ | 학술논문

目前,大多数多目标进化算法采用为单目标优化所设计的重组算子。通过证明或实验分析了几个典型的单目标优化重组算子并不适合某些多目标优化问题。提出了基于分解技术和混合高斯模型的多目标优化算法(multiobjective evolutionary algorithm based on decomposition and mixture Gaussian models,简称MOEA/D-MG)。该算法首先采用一个改进的混合高斯模型对群体建模并采样产生新个体,然后利用一个贪婪策略来更新群体。针对具有复杂Pareto前沿的多目标优化问题的测试结果表明,对给定的大多数测试题,该算法具有良好的效果。
목전,대다수다목표진화산법채용위단목표우화소설계적중조산자。통과증명혹실험분석료궤개전형적단목표우화중조산자병불괄합모사다목표우화문제。제출료기우분해기술화혼합고사모형적다목표우화산법(multiobjective evolutionary algorithm based on decomposition and mixture Gaussian models,간칭MOEA/D-MG)。해산법수선채용일개개진적혼합고사모형대군체건모병채양산생신개체,연후이용일개탐람책략래경신군체。침대구유복잡Pareto전연적다목표우화문제적측시결과표명,대급정적대다수측시제,해산법구유량호적효과。
Recombination operators used in most current multiobjective evolutionary algorithms (MOEAs) were originally designed for single objective optimization. This paper demonstrates that some widely used recombination operators may not work well for multiobjective optimization problems (MOPs), and proposes a multiobjective evolutionary algorithm based on decomposition and mixture Gaussian models (MOEA/D-MG). In the algorithm, a reproduction operator based on mixture Gaussian models is used to model the population distribution and sample new trails solutions, and a greedy replacement scheme is then applied to update the population by the new trial solutions. MOEA/D-MG is applied to a variety of test instances with complicated Pareto fronts. The extensive experimental results indicate that MOEA/D-MG is promising for dealing with these continuous MOPs.