Development and applications of an evolutionary multiobjective optimization algorithm for many-objective problems

在本文中，我们研究了进化多目标优化（EMO）算法在具有大量冲突目标的多目标问题中高效搜索帕累托最优解的能力。我们还研究了EMO算法的搜索能力与多目标问题中目标之间的依赖关系。在此基础上，我们提出了一种新的交叉算子用于多样性维护，一种近似方法用于高效的超体积计算，以及使用多个标度函数用于高效的多目标搜索。

In this paper, we examined the ability of evolutionary multiobjective optimization(EMO) algorithms to efficiently search for Pareto-optimal solutions of many-objective problems with a large number of conflicting objectives. We also examined the relation between the search ability of EMO algorithms and the dependency among objectives of many-objective problems. Based on these examinations, we proposed a new crossover operator for diversity maintenance, an approximation method for efficient hypervolume calculation, and the use of multiple scalarizing functions for efficient multiobjective search.