论文标题
用于多目标优化的混合自适应进化算法
Hybrid Adaptive Evolutionary Algorithm for Multi-objective Optimization
论文作者
论文摘要
多目标优化进化算法（MOEAS）的主要困难是如何找到能够融入具有高度多样性的真正帕累托阵线的合适解决方案。大多数现有的方法已经证明了涉及两个和三个目标的各种实际问题的利基，在依赖EA参数的依赖方面面临着重大挑战。此外，设定此类参数的过程被认为是耗时的，几项研究工作试图解决这个问题。本文提出了一种新的多目标算法，作为称为Mohaea的杂化自适应进化算法（HAEA）的扩展。 Mohaea允许通过解决基于优势和分解方法的多目标问题解决操作员概率（速率）的应用动态适应。在十个广泛使用的多目标测试问题上，将Mohaea与MoeA/D，Moea/D，Moea/D-Awa和NSGA-II进行了比较。实验结果表明，Mohaea在如何在帕累托阵线上找到一组覆盖良好且分布良好的点方面优于基准算法。
The major difficulty in Multi-objective Optimization Evolutionary Algorithms (MOEAs) is how to find an appropriate solution that is able to converge towards the true Pareto Front with high diversity. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges in the dependency of the selection of the EA parameters. Moreover, the process of setting such parameters is considered time-consuming, and several research works have tried to deal with this problem. This paper proposed a new Multi-objective Algorithm as an extension of the Hybrid Adaptive Evolutionary algorithm (HAEA) called MoHAEA. MoHAEA allows dynamic adaptation of the application of operator probabilities (rates) to evolve with the solution of the multi-objective problems combining the dominance- and decomposition-based approaches. MoHAEA is compared with four states of the art MOEAs, namely MOEA/D, pa$λ$-MOEA/D, MOEA/D-AWA, and NSGA-II on ten widely used multi-objective test problems. Experimental results indicate that MoHAEA outperforms the benchmark algorithms in terms of how it is able to find a well-covered and well-distributed set of points on the Pareto Front.
