Reddy Kumeshan, Saha Akshay K
Discipline of Electrical, Electronics & Computer Engineering, University of KwaZulu-Natal, 238 Mazisi Kunene Road, Durban, 4041, South Africa.
Heliyon. 2022 Oct 13;8(10):e11027. doi: 10.1016/j.heliyon.2022.e11027. eCollection 2022 Oct.
Swarm-based Metaheuristic Optimization Techniques (MOT) are the dominant among all techniques, particularly owing to their simple nature and robust performance. The Whale Optimization Algorithm (WOA), a swarm-based MOT inspired by the hunting strategy of the humpback whale, has thus far shown promising results. However, like all MOT, the WOA is not without drawbacks. These demerits are a slow convergence rate and poor exploitation capability. This may prove to be problematic when applied to optimization problems requiring high precision results. Over the past few years, there has been proposed modifications to the conventional algorithm. However, experimental analysis highlights the need to further enhance the properties of the algorithm. This work proposes an enhanced WOA for exploitation capability and stability enhancement. The proposed algorithm introduces various modifications to the position update equations of the conventional algorithm, as well as a modified algorithm structure. The proposed algorithm was compared to various state-of-the-art MOT, as well as modified WOA proposed in recent literature. When applied to the CEC 2019 benchmark functions, the proposed algorithm produced the best result in 7 of the 10 test and had the most superior overall placement. When applied to practical problems, the algorithm once again demonstrated superiority. In addition, it was observed that the proposed algorithm exhibited a superior convergence rate to the other compared techniques.
基于群体的元启发式优化技术（MOT）在所有技术中占主导地位，特别是由于其简单的性质和强大的性能。鲸鱼优化算法（WOA）是一种受座头鲸捕食策略启发的基于群体的MOT，迄今为止已显示出有前景的结果。然而，与所有MOT一样，WOA也并非没有缺点。这些缺点是收敛速度慢和开发能力差。当应用于需要高精度结果的优化问题时，这可能会成为问题。在过去几年中，人们对传统算法提出了改进。然而，实验分析突出了进一步增强算法性能的必要性。这项工作提出了一种增强的WOA，以提高开发能力和稳定性。所提出的算法对传统算法的位置更新方程进行了各种修改，并改进了算法结构。将所提出的算法与各种最新的MOT以及最近文献中提出的改进WOA进行了比较。当应用于CEC 2019基准函数时，所提出的算法在10个测试中的7个中产生了最佳结果，并且具有最优越的总体排名。当应用于实际问题时，该算法再次显示出优越性。此外，观察到所提出的算法与其他比较技术相比具有优越的收敛速度。
