基于混合反向学习策略的鲸鱼优化算法

A whale optimization algorithm based on hybrid reverse learning strategy

针对鲸鱼优化算法(WOA)在解决高维复杂问题时存在收敛速度慢、全局搜索能力不足的问题,提出一种最优最差个体混合反向学习的WOA(MWOA)。首先,引入一种自适应惯性权重,用于调节寻优前期的步长和寻优后期的种群多样性;其次,提出一种混合反向学习策略并将其融入WOA,以提高算法的收敛精度;最后,引入一种参数非线性衰减策略,以提高其在高维度以及复杂问题上的探索开发能力和收敛速度。将MWOA与WOA、MS-WOA、IWOA对10个基准函数的优化效果进行比较,结果表明MWOA在收敛速度、优化精度上相较对比算法均有所提升。另外,将MWOA与CODE、CPSO、EGWO和DIHS进行比较,结果表明MWOA具有较好的收敛精度。

Aiming at the problem of slow convergence speed and insufficient global search ability of whale optimization algorithm(WOA)in solving high-dimensional complex problems,a whale optimization algorithm based on hybrid reverse learning strategy(MWOA)is presented.Firstly,an adaptive inertial weight is introduced to adjust the step length in the early stage of the optimization and the population diversity in the later stage of the optimization.Secondly,a hybrid reverse learning strategy is proposed and integrated into WOA to improve the convergence accuracy of the algorithm.Finally,a nonlinear parameter attenuation strategy is introduced to improve its exploration and mining ability and convergence speed on high dimensions and complex problems.The optimization effects of MWOA,WOA,MS-WOA and IWOA on 10 benchmark functions are compared,and the result shows that MWOA improves the convergence speed and optimization accuracy compared with the other algorithms.Furthermore,the comparison among MWOA,CODE,CPSO,EGWO and DIHS shows that MWOA has better convergence accuracy.