求解大规模优化问题的改进鲸鱼优化算法

Improved whale optimization algorithm for large scale optimization problems

提出一种基于非线性收敛因子的改进鲸鱼优化算法(简记为IWOA)用于求解大规模复杂优化问题.为算法全局搜索奠定基础,在搜索空间中利用对立学习策略进行初始化鲸鱼个体位置;设计一种随进化迭代次数非线性变化的收敛因子更新公式以协调WOA算法的探索和开发能力;对当前最优鲸鱼个体执行多样性变异操作以减少算法陷入局部最优的概率.选取15个大规模(200维、500维和1000维)标准测试函数进行数值实验,结果表明,IWOA在求解精度和收敛速度方面明显优于其他对比算法.

An improved whale optimization algorithm （WOA） based on nonlinear convergence factor, named IWOA, is proposed for solving large scale complicated optimization problems. In the proposed algorithm, opposition-based learning strategy is used to initial the whale individuals＇ position in the search space, which strengthened the diversity of individuals in the global searching process. A novel nonlinearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation. It then disturbed the current optimal individual by diversity mutation operator in the process of the search so as to avoid the possibility of falling into local optimum. Simulation experiments were conducted on the 15 large scale （200, 500, and 1000 dimension） conventional test functions. The experimental results show that the proposed IWOA has better performance in solution precision and convergence rate than other comparison methods.