Harris Hawks optimization algorithm based on craziness and adaptiveness
To solve the slow convergence velocity and low-resolution precision in the evolutionary process of the standard Harris Hawks optimization(HHO)algorithm,an improved algorithm,called the crazy and adaptive HHO(CAHHO)algorithm,is proposed in this paper.A tent chaotic sequence is introduced to initiate the individuals'positions,which can strengthen the diversity of initiated individuals.The crazy operator is introduced at the prey source position to increase the diversity of the population.A new formula is obtained from the prey and substituted into the adaptive inertia weight to enhance and balance the search problem in the research process,including the global and local search ability of the algorithm.The efficiency of CAHHO is evaluated using statistical analysis,convergence rate analysis,and classical benchmark functions.The results show that CAHHO has good global search ability and solving robustness.Meanwhile,its optimization accuracy and convergence speed are more power-ful than those of the standard algorithm.Notably,the improved algorithm has better performance in solving high-di-mension and multimodal functions.Meanwhile,the algorithm is suitable for applications to actual optimization prob-lems.
万方数据
维普
