摘要
标准的缎蓝园丁鸟优化算法存在收敛速度慢、寻优精度低和易陷入局部最优等缺点,为此,提出了一种基于混合策略的缎蓝园丁鸟优化算法。首先,在种群初始化时,通过引入Logistic混沌映射,使初始种群能够均匀分布;其次,求偶亭位置更新时,加入了指数惯性权重,平衡了算法的全局和局部搜索能力,从而提升了算法的全局收敛速度;最后又在求偶亭位置变异时,引入了Levy飞行变异,提升了种群的多样性,使算法跳出局部最优。
The standard satin bowerbird optimization algorithm(SBO)has the problems of slow convergence speed,low precision and easy to fall into local optimization,therefore,a hybrid strategy based satin bowerbird optimization algorithm was proposed.First of all,the initial population can be uniformly distributed by introducing Logistic chaos map during the initial population initialization.Sec⁃ondly,when the bower position is updated,the exponential inertia weight is added to balance the global and local search ability of the algorithm,thus improving the global convergence speed of the algorithm;Then,Levy flight mutation is introduced when the bower posi⁃tion is changed,which improves the diversity of the population and makes the algorithm jump out of the local optimization.
作者
曹灿
高鹰
李宁
郭晓语
Cao Can;Gao Ying;Li Ning;Guo Xiaoyu(School of Computer Science and Cyber Engineering,Guangzhou University,Guangzhou 510000)
出处
《现代计算机》
2021年第29期1-9,共9页
Modern Computer
基金
北航北斗技术成果转化及产业化资金资助项目(BARI2004)。
