The Improvements And Applications Of Artificial Bee Colony Algorithm

Artificial Bee Colony algorithm (ABC), proposed by Dervis Karaboga fromTurkey in2005, is a kind of swarm intelligence algorithm. ABC algorithm hasbeen used to solve feed-forward neural networks training, distribution networkconfiguration, multi-objective optimization problem and so on. So far, ABCalgorithm attracts more and more attention of scholar and becomes researchhotpots of computational intelligence. But ABC also has some deficiencies throughanalyzing, such as slow speed of convergence, slum into local optimum. It needs toimprove the performance of ABC from the basic theory, algorithm design and soon. Besides, the application scope ofABC needed to be expanded.The paper makes some research of ABC pointed at the above problems, andthe obtained results can be summarized as follows:(1)In the view of improving the kinematics equations emerged in ABC, thepaper introduces Cauchy distribution to the employed bee stage, and Lévy flight toscout bee stage, proposes an improved Artificial Bee Colony algorithm withCauchy mutation(CABC). The simulation results show that CABC has betterperformance.(2)In the view of algorithm fusion, the paper proposes an improved algorithm,Artificial Bee Colony algorithm guided by complex method (CMABC),by usingcomplex method to guided the employed bee and scout bee to search new foodsource.Also, CMABC is used to solve nonlinear equation systems.(3) In the view of algorithm fusion, the paper uses eagle strategy to proposean Artificial Bee Colony algorithm with two stage eagle strategies (ETABC), andproves the convergence of it. Then ETABC is used to solve constrainedoptimization problems.