In order to solve the problem of imbalance between the search capability and the develop-ment capability of the original Harris hawks optimization(HHO)algorithm,an adaptive learning factor is added to dynamically adjust the search radius during local development.By judging the frequency of random walks,the exploration near the optimal position is strengthened or weakened to better balance the development at the extreme position and the field exploration.At the same time,the Sin chaos model is used to initialize the population distribution.In terms of the optimiza-tion performance of convergence accuracy and speed,a cross-literature comparison is made using a variety of variable dimension test functions.The results show that the improved algorithm has bet-ter optimization ability.
关键词
HHO/群体智能优化/学习因子/动态调整搜索半径
Key words
HHO/swarm intelligence optimization/learning factor/dynamic adjustment of search radius
维普
万方数据