论文标题
决策和目标空间离散化对进化多目标优化算法的性能的影响
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms
论文作者
论文摘要
最近，文献中已经讨论了决策和目标空间的离散化。在一些研究中，结果表明，决策空间离散化改善了连续多目标测试问题进化多目标优化（EMO）算法的性能。在其他研究中，结果表明，客观空间离散化改善了组合多目标问题的性能。但是，在文献中尚未研究两个空间同时离散的影响。在本文中，我们研究了决策空间离散化，客观空间离散化和同时离散化对NSGA-II通过计算实验对DTLZ和WFG问题的性能的影响。使用有关决策变量数量和目标数量的各种设置，我们的实验是在四种类型的问题上进行的：标准问题，大规模问题，多目标问题和大规模的多项目标问题。我们表明，决策空间离散化对大规模问题具有积极作用，并且客观空间离散化对多目标问题具有积极作用。我们还显示两个空间的离散化对于大规模的多目标问题很有用。
Recently, the discretization of decision and objective spaces has been discussed in the literature. In some studies, it is shown that the decision space discretization improves the performance of evolutionary multi-objective optimization (EMO) algorithms on continuous multi-objective test problems. In other studies, it is shown that the objective space discretization improves the performance on combinatorial multi-objective problems. However, the effect of the simultaneous discretization of both spaces has not been examined in the literature. In this paper, we examine the effects of the decision space discretization, objective space discretization and simultaneous discretization on the performance of NSGA-II through computational experiments on the DTLZ and WFG problems. Using various settings about the number of decision variables and the number of objectives, our experiments are performed on four types of problems: standard problems, large-scale problems, many-objective problems, and large-scale many-objective problems. We show that the decision space discretization has a positive effect for large-scale problems and the objective space discretization has a positive effect for many-objective problems. We also show the discretization of both spaces is useful for large-scale many-objective problems.
