In recent years, there has been growing interest in using metaheuristic algorithms to solve various complex engineering optimization problems. Most of the real-world problems comprise of more than one objective. Due to the inherent difficulty of such problems and lack of proficiency, researchers in different domains often aggregate multiple objectives and use single-objective optimization algorithms to solve them. However, the aggregation-based methods fail to solve the multi-objective problems (MOPs) effectively. Several multi-objective evolutionary algorithms (MOEAs) have been proposed and are being used to solve such problems in the past few years. In this paper, we propose an Emulous Mechanism-based multi-objective Moth–Flame Optimization (EMMFO) algorithm, where the moth positions are updated based on the pairwise competitions between the moths in each generation. The proposed EMMFO is tested on a diverse set of multi-objective benchmark functions like ZDT, DTLZ, WFG, CEC09 special session test suites and four constrained engineering design problems. The results are compared with various state-of-the-art multi-objective algorithms like NSGAII, SPEA2, PESA2, MOEA/D, MOPSO, MOACO, NSMFO, IEMO, CLPSO-LS, MOEA/D-CRA, PAL-SAPSO, and MORBABC/D. Extensive experimental results demonstrate superior optimization performance of the proposed algorithm.

近年来，使用元启发式算法来解决各种复杂的工程优化问题的兴趣日益浓厚。大多数现实世界中的问题都包含多个目标。由于此类问题的内在困难和缺乏熟练度，不同领域的研究人员经常汇总多个目标，并使用单目标优化算法来解决这些问题。但是，基于聚集的方法无法有效解决多目标问题（MOP）。已经提出了几种多目标进化算法（MOEA），并在过去几年中用于解决这些问题。在本文中，我们提出了一种基于弹性机制的多目标飞蛾-火焰优化（EMMFO）算法，根据每一代飞蛾之间的成对竞争来更新飞蛾位置。提议的EMMFO已在多种多目标基准测试功能上进行了测试，例如ZDT，DTLZ，WFG，CEC09特殊会话测试套件和四个受约束的工程设计问题。将结果与各种最新的多目标算法进行比较，例如NSGAII，SPEA2，PESA2，MOEA / D，MOPSO，MOACO，NSMFO，IEMO，CLPSO-LS，MOEA / D-CRA，PAL-SAPSO，和MORBABC / D。大量的实验结果证明了该算法的优越的优化性能。将结果与各种最新的多目标算法进行比较，例如NSGAII，SPEA2，PESA2，MOEA / D，MOPSO，MOACO，NSMFO，IEMO，CLPSO-LS，MOEA / D-CRA，PAL-SAPSO，和MORBABC / D。大量的实验结果证明了该算法的优越的优化性能。将结果与各种最新的多目标算法进行比较，例如NSGAII，SPEA2，PESA2，MOEA / D，MOPSO，MOACO，NSMFO，IEMO，CLPSO-LS，MOEA / D-CRA，PAL-SAPSO，和MORBABC / D。大量的实验结果证明了该算法的优越的优化性能。