混沌精英哈里斯鹰优化算法

Chaotic elite Harris hawks optimization algorithm

针对哈里斯鹰优化(HHO)算法存在的收敛精度低、收敛速度慢、易于陷入局部最优的不足,提出了一种混沌精英哈里斯鹰优化(CEHHO)算法。首先,引入精英等级制度策略,以充分利用优势种群来增强种群多样性以及提升算法收敛速度和精度;其次,利用Tent混沌映射调整算法关键参数;然后,使用一种非线性能量因子调节策略来平衡算法的开发与探索;最后,使用高斯随机游走策略对最优个体施加扰动,并在算法停滞时,利用随机游走策略使算法有效跳出局部最优。通过对20个基准测试函数在不同维度下进行仿真实验,来评估算法的寻优能力。实验结果表明,改进算法的表现优于鲸鱼优化算法(WOA)、灰狼优化(GWO)算法、粒子群优化(PSO)算法和生物地理优化(BBO)算法,性能较原始HHO算法有明显提升,验证了改进算法的有效性。

Aiming at the shortcomings of Harris Hawks Optimization(HHO)algorithm,such as low convergence accuracy,low convergence speed and being easy to fall into local optimum,a Chaotic Elite HHO(CEHHO)algorithm was proposed.Firstly,the elite hierarchy strategy was introduced to make full use of the dominant population to enhance the population diversity and improve the convergence speed and accuracy of the algorithm.Secondly,the Tent chaotic map was used to adjust the key parameters of the algorithm.Thirdly,a nonlinear energy factor adjustment strategy was adopted to balance the exploitation and exploration of the algorithm.Finally,the Gaussian random walk strategy was used to disturb the optimal individual,and when the algorithm was stagnant,the random walk strategy was used to make the algorithm jump out of the local optimum effectively.Through the simulation experiments of 20 benchmark functions in different dimensions,the optimization ability of the algorithm was evaluated.Experimental results show that the improved algorithm outperforms Whale Optimization Algorithm(WOA),Grey Wolf Optimization(GWO)algorithm,Particle Swarm Optimization(PSO)algorithm,and Biogeography-Based Optimization(BBO)algorithm,and the performance of this algorithm is significantly better than that of original HHO algorithm,which prove the effectiveness of the improved algorithm.