基于排序选择和精英引导的改进人工蜂群算法
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摘要
针对人工蜂群算法收敛速度较慢、收敛精度不高的问题,提出一种基于排序选择和精英引导的改进人工蜂群算法.分析观察蜂概率选择方法在适应值变化时对于精英个体优选的不足,提出一种排序选择方法,用以替代概率选择方法,从而提高算法的收敛速度.利用精英个体对搜索的引导作用,分别提出针对采蜜蜂和观察蜂的改进邻域搜索方程,从而提高算法的搜索效率.与其他人工蜂群算法的对比结果表明,所提出的改进方法能够有效提升算法的收敛速度和收敛精度.
In order to solve the problem of low convergence speed and low convergence accuracy of an artificial bee colony algorithm, an improved artificial bee colony algorithm based on ranking selection and elite guidance is proposed.The probability selection method of onlooker bees is weak to select the elite individual when the fitness value is changing,therefore, a ranking selection method is proposed to replace that of probability selection for improving the convergence speed of the algorithm. To improve the search e?ciency, two new neighborhood search equations for emplyed bees and onlooker bees respectively are proposed by using the elite guidance. Compared with other artificial bee colony algorithms,the results show that the proposed algorithm can e?ectively improve the convergence speed and convergence accuracy.
In order to solve the problem of low convergence speed and low convergence accuracy of an artificial bee colony algorithm, an improved artificial bee colony algorithm based on ranking selection and elite guidance is proposed.The probability selection method of onlooker bees is weak to select the elite individual when the fitness value is changing,therefore, a ranking selection method is proposed to replace that of probability selection for improving the convergence speed of the algorithm. To improve the search e?ciency, two new neighborhood search equations for emplyed bees and onlooker bees respectively are proposed by using the elite guidance. Compared with other artificial bee colony algorithms,the results show that the proposed algorithm can e?ectively improve the convergence speed and convergence accuracy.
引文
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[12]Ebrahimnejad A,Tavana M,Alrezaamiri H.A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights[J].Measurement,2016,93(11):48-56.
[13]Zhu G,Kwong S.Gbest-guided artificial bee colony algorithm for numerical function optimization[J]Applied Mathematics and Computation,2010,217(7):3166-3173.
[14]Gao W F,Liu S Y,Huang L L.A novel artificial bee colony algorithm based on modified search equation and orthogonal learning[J].IEEE Trans Cybern,2013,43(3):1011-1024.
[15]Karaboga D,Gorkemli B.A quick artificial bee colony(qABC)algorithm and its performance on optimization problems[J].Applied Soft Computing2014,23(5):227-238.
[16]Gao W,Liu S.Improved artificial bee colony algorithm for global optimization[J].Information Processing Letters2011,111(17):871-882.
[17]Li G,Cui L,Fu X,et al.Artificial bee colony algorithm with gene recombination for numerical function optimization[J].Applied Soft Computing,201752(3):146-159.
[18]Gao W F,Liu S Y.A modified artificial bee colony algorithm[J].Computers&Operations Research,201239(3):687-697.
[19]Gao W F,Huang L L,Liu S Y,et al.Artificial bee colony algorithm based on information learning[J].IEEE Trans Cybern,2015,45(12):2827-2839.
[2]Karaboga D,Basturk B.A powerful and efficient algorithm for numerical function optimization:Artificial bee colony(ABC)algorithm[J].J of Global Optimization2007,39(3):459-471.
[3]Yu K,Wang X,Wang Z.Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization[J].Knowledge-Based Systems,2016,96(3):156-170.
[4]Chuang Y C,Chen C T,Hwang C.A real-coded genetic algorithm with a direction-based crossover operator[J]Information Sciences,2015,305(6):320-348.
[5]Yang Q,Chen W N,Yu Z T,et al.Adaptive multimodal continuous ant colony optimization[J].IEEE Trans on Evolutionary Computation,2017,21(2):191-205.
[6]Cheng J,Zhang G,Caraffini F,et al.Multicriteria adaptive differential evolution for global numerical optimization[J].Integrated Computer-Aided Engineering,2015,22(2):103-107.
[7]Karaboga D,Gorkemli B,Ozturk C,et al.Acomprehensive survey:Artificial bee colony(ABC)algorithm and applications[J].Artificial Intelligence Review,2012,42(1):21-57.
[8]Huo Y,Zhuang Y,Gu J,et al.Discrete gbest-guided artificial bee colony algorithm for cloud service composition[J].Applied Intelligence,2014,42(4):661-678.
[9]Bai W,Eke I,Lee K Y.An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem[J].Control Engineering Practice,2017,61(4):163-172.
[10]Tran D H,Cheng M Y,Cao M T.Hybrid multiple objective artificial bee colony with differential evolution for the time-cost-quality tradeoff problem[J]Knowledge-Based Systems,2015,74(1):176-186.
[11]Ozturk C,Hancer E,Karaboga D.Dynamic clustering with improved binary artificial bee colony algorithm[J]Applied Soft Computing,2015,28(3):69-80.
[12]Ebrahimnejad A,Tavana M,Alrezaamiri H.A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights[J].Measurement,2016,93(11):48-56.
[13]Zhu G,Kwong S.Gbest-guided artificial bee colony algorithm for numerical function optimization[J]Applied Mathematics and Computation,2010,217(7):3166-3173.
[14]Gao W F,Liu S Y,Huang L L.A novel artificial bee colony algorithm based on modified search equation and orthogonal learning[J].IEEE Trans Cybern,2013,43(3):1011-1024.
[15]Karaboga D,Gorkemli B.A quick artificial bee colony(qABC)algorithm and its performance on optimization problems[J].Applied Soft Computing2014,23(5):227-238.
[16]Gao W,Liu S.Improved artificial bee colony algorithm for global optimization[J].Information Processing Letters2011,111(17):871-882.
[17]Li G,Cui L,Fu X,et al.Artificial bee colony algorithm with gene recombination for numerical function optimization[J].Applied Soft Computing,201752(3):146-159.
[18]Gao W F,Liu S Y.A modified artificial bee colony algorithm[J].Computers&Operations Research,201239(3):687-697.
[19]Gao W F,Huang L L,Liu S Y,et al.Artificial bee colony algorithm based on information learning[J].IEEE Trans Cybern,2015,45(12):2827-2839.
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