Systematic Analyses of Multi-Objective Evolutionary Algorithms applied to Real-World Problems using Statistical Design of Experiments
Systematic Analyses of Multi-Objective Evolutionary Algorithms applied to Real-World Problems using Statistical Design of Experiments
复制标题
使用实验统计设计对应用于现实世界问题的多目标进化算法进行系统分析
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
N. Henkenjohann
中科院分区：
文献类型：
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作者：
J. Mehnen;T. Michelitsch;T. Bartz;N. Henkenjohann
求解多目标优化问题是一项具有挑战性的任务，需要高效的软件工具和系统的分析方法。本文将进化策略(ES)和NSGA II两种进化多目标优化算法应用于两个复杂的实际问题。根据统计设计方案对进化算法的参数设置进行了选择和优化。介绍了一种新的衡量帕累托阵面质量的排序方法。模具温度控制系统的布局和电梯的调度显示出典型的复杂性方面，这是说明解决现实世界多目标优化问题的系统方法所必需的。
Solving multi-objective optimization problems is a challenging task that demands efficient software tools and systematic analytical approaches. In this paper two evolutionary multi-objective optimization algorithms – namely the evolution strategy (ES) and the NSGA II – are applied to two complex real-world problems. The parameter settings of the evolutionary algorithms have been chosen and optimized according to statistical design plans. A new ranking method for measuring the quality of pareto-fronts is introduced. The layout of mold temperature control systems and the scheduling of elevators show typical complexity aspects that are neccessary to illustrate a systematic approach of solving real-world multi-objective optimization problems.