Whale optimization algorithm (WOA) imitates social conduct of humpback whales which is inspired by bubble net hunting strategy of humpback whales. In the present study, Cosine adapted modified whale optimization algorithm (CamWOA) which is a modified version of WOA, has been proposed where cosine function is incorporated for the selection of control parameter “d” which governs the position of whales during optimization process. Also, correction factors are employed to modify the movement of search agents during the search process. These changes provide a proper balance between exploration and exploitation phases in CamWOA technique. The performance of CamWOA is analyzed by testing on a set of benchmark functions and compared with other state-of-the-art algorithms. It is observed that CamWOA outperforms other state-of-the-art metaheuristic algorithms in majority of benchmark functions. The efficiency of CamWOA is also evaluated by solving a multiobjective engineering problem pertaining to control of switched reluctance motor. The simulation results confirm that CamWOA yields very promising and competitive results compared to that of WOA and other metaheuristic optimization algorithms.

中文翻译：

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用于控制开关磁阻电机的余弦自适应修正鲸鱼优化算法

鲸鱼优化算法（WOA）模仿座头鲸的社会行为，其灵感来自座头鲸的泡泡网狩猎策略。在本研究中，提出了余弦自适应修正鲸鱼优化算法 (CamWOA)，它是 WOA 的修改版本，其中结合余弦函数来选择控制参数“ d”，它在优化过程中控制鲸鱼的位置。此外，校正因子用于在搜索过程中修改搜索代理的移动。这些变化在 CamWOA 技术的探索和开发阶段之间提供了适当的平衡。通过测试一组基准函数并与其他最先进的算法进行比较，分析了 CamWOA 的性能。据观察，CamWOA 在大多数基准函数中都优于其他最先进的元启发式算法。还通过解决与开关磁阻电机控制有关的多目标工程问题来评估 CamWOA 的效率。模拟结果证实，与 WOA 和其他元启发式优化算法相比，CamWOA 产生了非常有前景和具有竞争力的结果。