首页> 外文会议>International Conference on Natural Computation >A Novel Epsilon-Dominance Multi-objective Evolutionary Algorithms for Solving DRS Multi-objective Optimization Problems#x0A0;#x0A0;#x0A0;
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A Novel Epsilon-Dominance Multi-objective Evolutionary Algorithms for Solving DRS Multi-objective Optimization Problems#x0A0;#x0A0;#x0A0;

机译:一种新的epsilon - 优势多目标进化算法,用于解决DRS多目标优化问题

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A new kind of multiobjective optimization model is constructed in this paper, which contains various solutions apart from the true Pareto-optimums but hardly dominated. These solutions are defined as dominance resistant solutions (DRSs). It is proved that the evolutionary algorithms based on Paretodominance relationship fail to find the true Pareto fronts for the DRS MOP. Hence a new algorithm based on varepsilon -dominance relationship, called varepsilon -dominance MOEA( EDMOEA), is proposed to improve the DRSs in population effectively. Finally, experiments on a set of DRS MOOPs and other regular test functions are conducted, the EDMOEA outperforms the NSGA-II, and can be applied easily to complex multiobjective optimization problems.
机译:本文构建了一种新的多目标优化模型,其包含除真正的据静冈,而且几乎占主导地位。这些解决方案定义为优势抗性解决方案(DRS)。事实证明,基于帕龟关系的进化算法未能找到DRS MOP的真正帕累托前线。因此,提出了一种基于Varepsilon-adbinance关系的新算法,称为Varepsilon-adbinance Moea(Edmoea),以有效地改善人口中的DRS。最后,进行了一组DRS MOOPS和其他常规测试功能的实验,EDMOEA优于NSGA-II,并且可以容易地应用于复杂的多目标优化问题。

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