Adriana Cortes Godinez, Luis Ernesto Mancilla Espinosa, E. M. Montes
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引用次数: 23
摘要
多目标问题的优化是当前一个重要的研究和发展领域。问题类型的重要性允许开发多种元启发式解决方案。为了确定哪一种多目标元启发式算法在某一问题上的性能最好，本文利用ZDT测试函数对两种多目标元启发式算法进行了实验比较:排序遗传算法No dominant - ii (NSGA-II)和多目标粒子群优化(MOPS)。基于不同的性能指标，对两种算法得到的结果进行了比较和分析，这些性能指标评估了解在Pareto前沿的离散性及其接近性。
An Experimental Comparison of Multiobjective Algorithms: NSGA-II and OMOPSO
The optimization of multi objective problems is currently an area of important research and development. The importance of type of problems has allowed the development of multiple metaheuristics for their solution. To determine which multi objective metaheuristic has the best performance with respect to a problem, in this article an experimental comparison between two of them: Sorting Genetic Algorithm No dominated-II (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPS) using ZDT test functions is made. The results obtained by both algorithms are compared and analyzed based on different performance metrics that evaluate both the dispersion of the solutions on the Pareto front, and its proximity to it.
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