论文标题
一种基于分解的大规模多模式多目标多目标优化算法
A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm
论文作者
论文摘要
多模式多目标多目标优化问题是一种特殊的多目标优化问题，具有多个帕累托子集。在本文中，我们提出了一种基于广泛使用的MOEA/D算法的高效多模式多模式优化算法。在我们提出的算法中，每个权重矢量都有其自身的子人群。通过清除机制和贪婪的去除策略，我们提出的算法可以有效地保留等效的帕累托最佳解决方案（即具有相同目标值的不同帕累托最佳解决方案）。实验结果表明，我们提出的算法可以在处理大型多模式的多目标优化问题时有效地保留决策空间中解决方案的多样性。
A multi-modal multi-objective optimization problem is a special kind of multi-objective optimization problem with multiple Pareto subsets. In this paper, we propose an efficient multi-modal multi-objective optimization algorithm based on the widely used MOEA/D algorithm. In our proposed algorithm, each weight vector has its own sub-population. With a clearing mechanism and a greedy removal strategy, our proposed algorithm can effectively preserve equivalent Pareto optimal solutions (i.e., different Pareto optimal solutions with same objective values). Experimental results show that our proposed algorithm can effectively preserve the diversity of solutions in the decision space when handling large-scale multi-modal multi-objective optimization problems.
