一种改进的混合鲸鱼优化算法研究

Research on an Improved Hybrid Whale Optimization Algorithm

在对鲸鱼优化算法(WOA)的数学模型深入研究时,发现传统的WOA算法在处理一些较为复杂的问题,或者对高维度空间进行搜索时还存在一些不足之处,如收敛速度慢、易陷入局部最优等缺点。针对这些问题,文章提出一种改进的混合鲸鱼优化算法,通过引入精英反向学习、非线性收敛因子和自适应调整搜索组合策略来提高算法收敛速度,弥补传统WOA算法的不足。实验结果表明,改进后的算法(IWOA)性能更优,有着广泛的应用前景。

In an in-depth study of the mathematical model of Whale Optimization Algorithm (WOA),it is found that the traditional WOA algorithm has some shortcomings when dealing with some more complex problems or searching for high-dimensional spaces,such as slow convergence speed,easy to fall into local optimality and other shortcomings.To address these problems,this paper proposes an improved hybrid whale optimization algorithm to improve the convergence speed of the algorithm and make up for the shortcomings of the traditional WOA algorithm by introducing elite reverse learning,nonlinear convergence factor and adaptive adjustment of the search combination strategy.Experimental results show that the improved algorithm (IWOA) has better performance and has wide application prospects.