论文标题
圆锥体主管：进化多物镜优化的方法
The Cone epsilon-Dominance: An Approach for Evolutionary Multiobjective Optimization
论文作者
论文摘要
我们提出了锥晶义义方法，以改善多物体进化算法的收敛性和多样性（MOEAS）。提出了一个锥体 - EPS-MOEA，并根据标准的帕累托关系（NSGA-II，NSGA-II*，SPEA2和簇状的NSGA-II）和Epsilon-Porminalance（EPS-MOEA）进行比较。比较既是根据计算复杂性和选择的四个性能指标来量化每种算法获得的最终结果质量的方法：收敛，多样性，超量和许多集合指标的覆盖范围。在实验部分中考虑了16个众所周知的基准问题，包括ZDT和DTLZ家族。为了评估算法之间可能的差异，对四个性能指标进行了精心设计的实验。获得的结果表明，锥体EPS-MOEA能够在所有考虑的性能指标上提出有效且平衡的性能。这些结果强烈支持以下结论：锥体EPS-MOEA是一种竞争方法，可以在收敛和对帕累托阵线的多样性之间取得有效的平衡，因此代表了解决多目标优化问题的有用工具。
We propose the cone epsilon-dominance approach to improve convergence and diversity in multiobjective evolutionary algorithms (MOEAs). A cone-eps-MOEA is presented and compared with MOEAs based on the standard Pareto relation (NSGA-II, NSGA-II*, SPEA2, and a clustered NSGA-II) and on the epsilon-dominance (eps-MOEA). The comparison is performed both in terms of computational complexity and on four performance indicators selected to quantify the quality of the final results obtained by each algorithm: the convergence, diversity, hypervolume, and coverage of many sets metrics. Sixteen well-known benchmark problems are considered in the experimental section, including the ZDT and the DTLZ families. To evaluate the possible differences amongst the algorithms, a carefully designed experiment is performed for the four performance metrics. The results obtained suggest that the cone-eps-MOEA is capable of presenting an efficient and balanced performance over all the performance metrics considered. These results strongly support the conclusion that the cone-eps-MOEA is a competitive approach for obtaining an efficient balance between convergence and diversity to the Pareto front, and as such represents a useful tool for the solution of multiobjective optimization problems.
