Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show good optimization performance for many-objective optimization problems (MaOPs). Especially, algorithms that employ a clustering algorithm with a topological structure (i.e., a network composed of nodes and edges) show superior optimization performance to other MOEAs for MaOPs with irregular Pareto optimal fronts (PFs). These algorithms, however, do not effectively utilize information of the topological structure in the search process. Moreover, the clustering algorithms typically used in conventional studies have limited clustering performance, inhibiting the ability to extract useful information for the search process. This paper proposes an adaptive reference vector-guided evolutionary algorithm using an adaptive resonance theory-based clustering with a topological structure. The proposed algorithm utilizes the information of the topological structure not only for reference vector adaptation but also for mating selection. The proposed algorithm is compared with 8 state-of-the-art MOEAs on 78 test problems. Experimental results reveal the outstanding optimization performance of the proposed algorithm over the others on MaOPs with various properties.
翻译:以集群为基础的多客观演进算法(MOEAs)的分解、多客观演进算法(MOEAs)具有基于集群的参照矢量适应性,这些算法在很多目标优化问题(MaOPs)方面表现良好。特别是,采用具有地形结构(即由节点和边缘组成的网络)的组合算法的算法(即由节点和边缘组成的网络)的算法,显示出优于具有不规则的Pareto最佳战线(PFS)的其他MOEAs的组合算法。然而,这些算法没有有效地利用搜索过程中的地形结构信息。此外,常规研究中通常使用的组合算法的组合法效果有限,限制了为搜索进程提取有用信息的能力。本文建议采用适应性参考矢量指导进化算法,采用适应性再共振理论的组合法,不仅用于参考矢量适应,而且用于交配方选择。提议的算法与78个测试问题的8个最先进的MOEAs比较。实验结果揭示了在MaOPs的各种属性上提议的算法的其余算法的杰出的优化表现。