Kalita Kanak, Pandya Sundaram B, Čep Robert, Jangir Pradeep, Abualigah Laith
Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600 062, India.
University Centre for Research & Development, Chandigarh University, Mohali, 140413, India.
Heliyon. 2024 Jun 17;10(12):e32911. doi: 10.1016/j.heliyon.2024.e32911. eCollection 2024 Jun 30.
Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel Many-Objective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.
多目标优化（MaO）是工程场景中的一个重要方面。在多目标优化算法（MaOAs）中，一个关键挑战是在多样性和收敛性之间取得平衡。MaOAs采用各种策略来增强选择压力以实现更好的收敛和/或实施额外措施来维持多样性。随着目标数量的增加，这个过程变得更加复杂，主要是由于在种群选择过程中实现收敛存在挑战。本文介绍了一种新颖的多目标蚁狮优化器（MaOALO），它以广受欢迎的蚁狮优化器算法为特色。该方法利用参考点、小生境保留和信息反馈机制（IFM）来增强种群的收敛性和多样性。已针对五个实际世界（RWMaOP1 - RWMaOP5）优化问题和标准问题类进行了广泛的实验测试，包括MaF1 - MaF15（用于5、9和15个目标）、DTLZ1 - DTLZ7（用于8个目标）。结果表明，在GD、IGD、SP、SD、HV和RT指标方面，MaOALO优于ARMOEA、NSGA - III、MaOTLBO、RVEA、MaOABC - TA、DSAE、RL - RVEA和MaOEA - IH算法。MaOALO的源代码可在以下网址获取：https://github.com/kanak02/MaOALO。
