智能系统学报2017，Vol.12Issue(5)：684-693,10.DOI:10.11992/tis.201612026
一种增强局部搜索能力的改进人工蜂群算法
Improved artificial bee colony algorithm based on enhanced local search
摘要
Abstract
The shortcomings of the artificial bee colony algorithm ( ABC ) are its uneven initial population distribution and weak local search. In this paper, we propose an ABC algorithm based on enhanced local search ( ESABC) . First, we employ a high-dimension chaotic system ( Lorenz system) to obtain the ergodic and regular initial populations and to avoid the blindness of random initialization in the population initialization stage. Then, we introduce improved fitness evaluation methods based on the logarithmic function to increase the differences between individuals, reduce selection pressure, and avoid premature convergence. Lastly, inspired by the differential evolution algorithm, we propose a new search tactic that uses the best individual in the contemporary population to guide the renewal of the next generation, and thereby enhance the local search ability. We examined the performance of the proposed approach with 12 classic testing functions and compared the results with the basic and other ABCs. As documented in the experimental results, the proposed algorithm exhibits good optimization performance and can improve both the accuracy and convergence speed of the algorithm.关键词
人工蜂群算法/高维混沌系统/适应度评价/搜索策略/优化算法/演化算法/收敛性分析/精度分析/智能算法Key words
artificial bee colony algorithm/high-dimension chaotic system/fitness evaluation/search tactics/optimization algorithm/evolutionary algorithm/convergence analysis/accuracy analysis/intelligent algorithm分类
信息技术与安全科学引用本文复制引用
刘晓芳,柳培忠,骆炎民,范宇凌..一种增强局部搜索能力的改进人工蜂群算法[J].智能系统学报,2017,12(5):684-693,10.基金项目
国家自然科学基金资助项目(61203242) （61203242）
物联网云计算平台建设资助项目(2013H2002) （2013H2002）
华侨大学研究生科研创新能力培育计划资助项目(1511322003). （1511322003）
