融合镜面反射学习和信息共享策略的白鲸优化算法

Beluga Whale Optimization Algorithm Integrating Specular Reflection Learning and Information Sharing Strategies

摘要: 针对白鲸优化算法(BWO)全局搜索能力差、优化精度低和易陷入局部最优等方面的局限，提出了一种融合镜面反射学习和信息共享策略的白鲸优化算法。为了增加白鲸群体中个体多样性，融合一种佳点集策略和镜面反射学习策略。在进行信息共享搜索策略时，部分白鲸向同伴所在领域相互获取信息，实现种群白鲸之间信息的共享与相互交流。最后，通过8个标准检验函数对改进的白鲸优化算法性能进行了全面评估，将其与其他几种算法进行了比较。仿真结果表明，改进的白鲸优化算法在迭代速度和收敛精度方面取得了显著的提升，并展现了出色的鲁棒性。

Abstract: Beluga whale optimization algorithm that combines specular reflection learning and information sharing strategy is proposed to address the limitations of beluga whale optimization (BWO) in terms of poor global search ability, low optimization accuracy, and susceptibility to local optima. In order to increase individual diversity in the beluga whale population, a best point set strategy and specular reflection learning strategy are integrated. When implementing information sharing search strategies, some beluga whales obtain information from their peers’ domains, achieving information sharing and mutual communication among populations of beluga whales. Finally, the performance of the improved beluga whale optimization was comprehensively evaluated using 8 standard test functions and compared with several other algorithms. The simulation results show that the improved beluga whale optimization has achieved significant improvements in iteration speed and convergence accuracy, and demonstrated excellent robustness.