Mahajan Raghav, Sharma Himanshu, Arora Krishan, Joshi Gyanendra Prasad, Cho Woong
School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, 144411, India.
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea.
Heliyon. 2024 Aug 16;10(17):e36425. doi: 10.1016/j.heliyon.2024.e36425. eCollection 2024 Sep 15.
The Gazelle Optimization Algorithm (GOA) is an innovative nature-inspired metaheuristic algorithm, designed to mimic the agile and efficient hunting strategies of gazelles. Despite its promising performance in solving complex optimization problems, there is still a significant scope for enhancing its efficiency and robustness. This paper introduces several novel variants of GOA, integrating adaptive strategy, Levy flight strategy, Roulette wheel selection strategy, and random walk strategy. These enhancements aim to address the limitations of the original GOA and improve its performance in diverse optimization scenarios. The proposed algorithms are rigorously tested on CEC 2014 and CEC 2017 benchmark functions, five engineering problems, and a Total Harmonic Distortion (THD) minimization problem. The results demonstrate the superior performance of the proposed variants compared to the original GOA, providing valuable insights into their applicability and effectiveness.
瞪羚优化算法（GOA）是一种创新的受自然启发的元启发式算法，旨在模仿瞪羚敏捷高效的狩猎策略。尽管它在解决复杂优化问题方面表现出了良好的性能，但仍有很大的空间来提高其效率和鲁棒性。本文介绍了几种GOA的新颖变体，融合了自适应策略、莱维飞行策略、轮盘赌选择策略和随机游走策略。这些改进旨在解决原始GOA的局限性，并提高其在各种优化场景中的性能。所提出的算法在CEC 2014和CEC 2017基准函数、五个工程问题以及一个总谐波失真（THD）最小化问题上进行了严格测试。结果表明，与原始GOA相比，所提出的变体具有卓越的性能，为它们的适用性和有效性提供了有价值的见解。
