An improved Harris hawks optimization algorithm based on elite guidance
Aiming at the problems that Harris Hawks Optimization(HHO)is easy to fall into local optimization and has slow convergence speed,an improved Harris Hawks Optimization algorithm based on elite guidance(EHHO)is proposed.Firstly,elite opposite learning is introduced,and the elite cen-ter is used as the symmetrical center for opposite learning to optimize the population structure and en-hance the ability of the algorithm to jump out of local optimum.Secondly,the elite evolution strategy is introduced,and the evolution based on Gaussian random mutation is carried out with elite individuals as the main body to improve the quality of the population and improve the convergence speed of the algo-rithm.Finally,an adaptive mechanism is introduced to dynamically adjust the selection probability of the two evolution modes in the elite evolution strategy to improve the stability of the algorithm.To veri-fy the effectiveness of the improved algorithm,15 benchmark functions are selected for simulation ex-periments.The experimental results show that the improved algorithm has obvious improvement in op-timization performance and robustness,and has certain competitiveness in optimization algorithms.
Harris hawks optimization(HHO)algorithmelite opposite learningelite evolution strategyGaussian random mutationadaptive mechanism
万方数据
维普
