摘要
针对人工蜂群算法在处理复杂问题时易陷入局部最优的不足,提出一种自适应人工蜂群算法(APABC)。通过蜂群寻蜜的加速度系数随搜索过程而动态适应变化来提高算法的局部搜索性能,引入搜索蜜源能力较差的观察蜂向能够寻觅到更多蜜源的引领蜂学习交互策略,来进一步提高算法的全局搜索性能。将APABC算法与ABC算法进行性能对比测试,测试结果表明文中算法具有较快的收敛速度和较高的寻优精度,计算结果优于传统的ABC算法。
An adaptive artificial bee colony(APABC)algorithm is proposed to eliminate the disadvantage of falling into local optimum when dealing with complex problems.By dynamically adapting to changes in the acceleration coefficient of bee colony honey seeking along with the search process,the local search performance of the algorithm is improved.The interaction strategy of observing bees with poor ability of honey source searching learning from leading bees which can find out more honey sources is introduced to further improve the overall search performance of the algorithm.The performance testing of APABC algorithm and ABC algorithm are performed.The testing results show that the proposed algorithm has fast convergence speed and high optimizing accuracy,and its calculation results are better than those of the classical ABC algorithms.
作者
徐洁
朱晶晶
牛思杰
汪志锋
XU Jie;ZHU Jingjing;NIU Sijie;WANG Zhifeng(School of Intelligent Manufacturing and Control Engineering,Shanghai Polytechnic University,Shanghai 201209,China)
出处
《现代电子技术》
北大核心
2024年第21期183-186,共4页
Modern Electronics Technique
