基于多元信息引导的人工蜂群算法OA北大核心CSTPCD

As one of the main ideas to improve the artificial bee colony(ABC)algorithm,the superior individuals are used to enhance the exploitative capability of the solution search equation.However,in the related works,the fitness infor-mation is often considered as the sole criterion for evaluating the individuals,which may easily cause some problems,e.g.,the premature convergence.In this work,an improved ABC variant is proposed based on multiple information guidance,called ABC-MIG.In ABC-MIG,three different solution search equations are designed by using the fitness,position,and similarity information,respectively,and these new solution search equations are used in different ways for the employed bee phase and onlooker bee phase.Meanwhile,to save the search experience for the scout bee phase,a modified neighbor-hood search strategy is used to handle the abandoned food sources.To verify the effectiveness of ABC-MIG,extensive ex-periments are carried out on the CEC2013 test suite and one real-world optimization problem,and six derivative algorithms and five well-known improved ABC variants are included in the performance comparison.The results confirm that ABC-MIG has very competitive performance,in terms of the result accuracy and convergence speed.