多目标优化的Pareto-MEC评分

Chengyi Sun, Wanzhen Wang, X.Z. Gao
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引用次数: 3

摘要

本文提出了一种新的多目标优化算法——得分帕累托思维进化计算(SP-MEC),它将帕累托理论引入到思维进化计算中,用于多目标优化。在我们的SP-MEC中,个体的选择是基于他们的分数,包括个体之间的帕累托优势度和密度信息。基于凸性、非凸性、离散性和非均匀性四个不同的测试问题,将SP-MEC与VEGA、NSGA、SPEA和Pareto-MEC进行了比较。特别是,Pareto-MEC和SPEA在解决各种优化问题方面都表现出了良好的性能。在测试问题上,SP-MEC在权衡前沿到帕累托最优前沿的距离、解的均匀性和解的扩展性三个指标上优于所有四种参考算法。SP-MEC和Pareto-MEC采用非个人终止准则代替了其他算法的预设代数。SP-MEC具有比VEGA、NSGA和SPEA更高的计算效率。与Pareto-MEC算法相比,SP-MEC算法的计算效率略低。但SP-MEC的溶液质量高于Pareto-MEC。因此,SP-MEC算法是一种强大的多目标优化算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scored Pareto-MEC for multi-objective optimization
This paper proposes a new multi-objective optimization algorithm - scored Pareto mind evolutionary computation (SP-MEC), which introduces the theory of Pareto into mind evolutionary computation (MEC) for the multi-objective optimization. In our SP-MEC, the selection of individuals is based on their scores that include the Pareto dominance and density information among the individuals. The SP-MEC is compared with the VEGA, NSGA, SPEA, and Pareto-MEC on the basis of four different test problems: convexity, non-convexity, discreteness, as well as non-uniformity. Especially, both the Pareto-MEC and SPEA have shown promising performances in solving various optimization problems. On the test problems, SP-MEC outperforms all the four reference algorithms concerning three measures: the distance from trade-off front to Pareto-optimal front, the uniformity of solutions, and the spread of solutions. Impersonal termination criterion is used in SP-MEC and Pareto-MEC instead of the preset number of generations in other algorithms. SP-MEC has a higher computational efficiency than the VEGA, NSGA, and SPEA. Compared with another our algorithm, Pareto-MEC, the computational efficiency of SP-MEC is a little lower. However, the solution quality of SP-MEC is higher than that of the Pareto-MEC. Therefore, it can be concluded the SP-MEC is a powerful algorithm for multi-objective optimization.
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