摘要
针对人工蜂群算法在以往研究中表现出探索局限性以及开发低效性等缺点,提出一种自适应收敛下的改进人工蜂群算法。该算法通过全域采样随机初始化保证初始解集完整性;选择概率计算中加入开采次数因子提升潜在较优解选中概率;结合余弦函数变化特点,对选中的个体进行全局最优个体引导下的自适应局部开发,提升局部开发精度。最后,通过不同灾害场景下与多个算法进行对比,结果表明改进后的算法具备更高的求解精度,更好的全局收敛性,能高效解决复杂灾害场景下的路径规划问题。
Aiming at the shortcomings of artificial bee colony algorithm in previous studies,such as exploration limitations and development inefficiency,an improved artificial bee colony algorithm with adaptive convergence is proposed.The algorithm uses global sampling and random initialization to ensure the integrity of the initial solution set.The mining times factor is added to the selection probability calculation to increase the probability of potential solutions.Combining the characteristics of the cosine function change,the selected individuals are subjected to adaptive partial development under the guidance of the global optimal individual to improve the accuracy of local development.Finally,through comparison with multiple algorithms in different disaster scenarios,the results show that the improved algorithm has higher solution accuracy,better global convergence,and can efficiently solve path planning problems in complex disaster scenarios.
作者
朱金磊
袁晓兵
裴俊
ZHU Jinlei;YUAN Xiaobing;PEI Jun(Science and Technology on Microsystem Laboratory,Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,Shanghai 201800,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《中国科学院大学学报（中英文）》
CSCD
北大核心
2023年第3期397-405,共9页
Journal of University of Chinese Academy of Sciences
基金
国家重点研发计划(2020YFC1511602)资助。
关键词
人工蜂群算法
全域采样
潜在较优解
路径规划
灾害场景
全局收敛性
artificial bee colony algorithm
global sampling
potential solution
path planning
disaster scenario
global convergence
