Dutta Saykat, Mallipeddi Rammohan, Das Kedar Nath
Department of Mathematics, National Institute of Technology Silchar, Silchar, India.
Department of Artificial Intelligence, School of Electronics Engineering, Kyungpook National University, Daegu, South Korea.
Sci Rep. 2022 Apr 27;12(1):6861. doi: 10.1038/s41598-022-10997-0.
In the last decade, numerous multi/many-objective evolutionary algorithms (MOEAs) have been proposed to handle multi/many-objective problems (MOPs) with challenges such as discontinuous Pareto Front (PF), degenerate PF, etc. MOEAs in the literature can be broadly divided into three categories based on the selection strategy employed such as dominance, decomposition, and indicator-based MOEAs. Each category of MOEAs have their advantages and disadvantages when solving MOPs with diverse characteristics. In this work, we propose a Hybrid Selection based MOEA, referred to as HS-MOEA, which is a simple yet effective hybridization of dominance, decomposition and indicator-based concepts. In other words, we propose a new environmental selection strategy where the Pareto-dominance, reference vectors and an indicator are combined to effectively balance the diversity and convergence properties of MOEA during the evolution. The superior performance of HS-MOEA compared to the state-of-the-art MOEAs is demonstrated through experimental simulations on DTLZ and WFG test suites with up to 10 objectives.
在过去十年中，人们提出了众多多目标进化算法（MOEA）来处理多目标问题（MOP），这些问题存在诸如不连续帕累托前沿（PF）、退化PF等挑战。根据所采用的选择策略，如基于支配、分解和基于指标的MOEA，文献中的MOEA大致可分为三类。在解决具有不同特征的MOP时，每类MOEA都有其优缺点。在这项工作中，我们提出了一种基于混合选择的MOEA，称为HS-MOEA，它是基于支配、分解和基于指标的概念的一种简单而有效的混合。换句话说，我们提出了一种新的环境选择策略，其中帕累托支配、参考向量和一个指标相结合，以在进化过程中有效平衡MOEA的多样性和收敛性。通过在具有多达10个目标的DTLZ和WFG测试套件上进行实验模拟，证明了HS-MOEA与现有MOEA相比具有优越的性能。
