中文摘要：

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 Gaussianmodels，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．

英文摘要：

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.