融合多策略改进的哈里斯鹰优化算法

Improved Harris Hawks Optimization Fused with Multiple Strategies

针对哈里斯鹰优化算法收敛精度低、易于早熟收敛、全局搜索与局部开发不平衡的问题,提出一种融合多策略的哈里斯鹰优化算法。利用佳点集初始化种群,提高初始解的质量;通过重新设置算法的条件,平衡算法的探索与开发;引入麻雀搜索算法中发现者位置更新公式对探索阶段进行改进,提升算法的全局搜索能力;采用柯西变异和高斯变异对最优解进行扰动,有效避免算法陷入局部最优。通过对6个基准测试函数进行仿真实验,与其他智能优化算法及其他改进的哈里斯鹰算法进行对比分析,实验结果表明改进方法的寻优能力优于对比算法。

The Harris Hawk optimization algorithm incorporating multiple strategies was proposed to solve the problems of low convergence accuracy,easy premature convergence and imbalance between global search and local exploitation.The algorithm improved the quality of the initial solution by setting good points to initialize the population.The exploration and development of the Harris Hawk optimization algorithm had been balanced by resetting the conditions for the algorithm to perform exploration or development.Furthermore,the improvement of exploration phase in sparrow search algorithm′s finder position update formula was introduced to enhance the global search ability of the algorithm.The algorithm could avoid being trapped in local optimum effectively by using the Cauchy variation and Gaussian variation to perturb the optimal solution.Through simulation experiments on six benchmark test functions and comparative analysis with other intelligent optimization algorithms and other improved Harris Hawk algorithms,the results showed that the improved method could outperform the comparison algorithms in terms of the search optimization ability.