基于多策略融合的改进人工蜂群算法
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摘要
针对标准人工蜂群算法存在易陷入局部最优、收敛速度慢等缺陷,提出一种基于多策略融合的改进人工蜂群算法。为了避免陷入局部最优,引入可调压排序选择策略,以保证种群的多样性;同时,通过跟随蜂阶段将线性调整全局引导策略、自适应动态调整因子策略与标准人工蜂群算法的更新策略组成一个动态调整策略集,通过比较食物源的当前质量值与上次迭代质量值对动态策略进行调整,以加快算法的收敛速度。利用标准测试函数进行实验仿真,结果表明该算法不仅提高了求解精度,而且加快了收敛速度,迭代次数明显减少。
To overcome the defects of convergence speed and the local optimum of artificial bee colony algorithm, this paper proposes an improved artificial bee colony algorithm based on multi-strategy fusion. In order to maintain the population diversity and avoid the local optimum, this paper imports the strategy of adjustable voltage ranking selection.To accelerate the convergence rate of artificial bee colony algorithm, a dynamic adjustment strategy set is composed of linear adjustment global guidance strategy, adaptive dynamic adjustment factor strategy and updating strategy of standard artificial swarm algorithm in following bee stage. The policy is dynamically adjusted by comparing the current update value of the food source with the last iterative update value. Through the simulation experiment on a suite of standard functions, the results show that the algorithm has a faster convergence rate and higher solution accuracy, and less number of iterations.
To overcome the defects of convergence speed and the local optimum of artificial bee colony algorithm, this paper proposes an improved artificial bee colony algorithm based on multi-strategy fusion. In order to maintain the population diversity and avoid the local optimum, this paper imports the strategy of adjustable voltage ranking selection.To accelerate the convergence rate of artificial bee colony algorithm, a dynamic adjustment strategy set is composed of linear adjustment global guidance strategy, adaptive dynamic adjustment factor strategy and updating strategy of standard artificial swarm algorithm in following bee stage. The policy is dynamically adjusted by comparing the current update value of the food source with the last iterative update value. Through the simulation experiment on a suite of standard functions, the results show that the algorithm has a faster convergence rate and higher solution accuracy, and less number of iterations.
引文
[1]Karaboga D.An idea based on honey bee swarm for numerical optimization,technical report-tr06[R].Computer Engineering Department,Engineering Faculty,Erciyes University,2005.
[2]Karaboga D,Basturk B.On the performance of Artificial Bee Colony(ABC)algorithm[J].Applied Soft Computing,2008,8(1):687-697.
[3]Wang Hui,Wu Zhijian.Multi-strategy ensemble artificial bee colony algorithm[J].Information Sciences,2014,279:587-603.
[4]毛力,周长喜,吴滨.基于当前最优解的分段搜索策略的人工蜂群算法[J].计算机科学,2015,42(12):263-267.
[5]Vitorino L N,Ribeiro S F.A mechanism based on Artificial Bee Colony to generate diversity in Particle Swarm Optimization[J].Neurocomputing,2015,148:39-45.
[6]Das S,Biswas S,Kundu S.Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization[J].Applied Soft Computing,2013,13(12):4676-4694.
[7]任聪,葛洪伟,杨金龙,等.引入混合蛙跳搜索策略的人工蜂群粒子群算法[J].计算机工程与应用,2015,51(22):38-41.
[8]罗浩,刘宇.一种强化互学习的人工蜂群算法[J].计算机工程与应用,2016,52(16):23-29.
[9]高卫峰,三阳,焦合华,等.采用人工蜂群搜索算子的粒子群算法[J].控制与决策,2012,27(6):833-838.
[10]颜丽燕,张桂珠.基于蜂群算法的多维Qo S云计算任务调度[J].计算机工程与科学,2016,38(4):648-655.
[11]Mallipeddi R,Mallipeddi S,Suganthan P N.Ensemble strategies with adaptive evolutionary programming[J].Information Sciences,2010,180(9):1571-1581.
[12]Karaboga D,Gorkemli B.A quick Artificial Bee Colony(qA BC)algorithm and its performance on optimization problems[J].Applied Soft Computing,2014,23(5):227-238.
[2]Karaboga D,Basturk B.On the performance of Artificial Bee Colony(ABC)algorithm[J].Applied Soft Computing,2008,8(1):687-697.
[3]Wang Hui,Wu Zhijian.Multi-strategy ensemble artificial bee colony algorithm[J].Information Sciences,2014,279:587-603.
[4]毛力,周长喜,吴滨.基于当前最优解的分段搜索策略的人工蜂群算法[J].计算机科学,2015,42(12):263-267.
[5]Vitorino L N,Ribeiro S F.A mechanism based on Artificial Bee Colony to generate diversity in Particle Swarm Optimization[J].Neurocomputing,2015,148:39-45.
[6]Das S,Biswas S,Kundu S.Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization[J].Applied Soft Computing,2013,13(12):4676-4694.
[7]任聪,葛洪伟,杨金龙,等.引入混合蛙跳搜索策略的人工蜂群粒子群算法[J].计算机工程与应用,2015,51(22):38-41.
[8]罗浩,刘宇.一种强化互学习的人工蜂群算法[J].计算机工程与应用,2016,52(16):23-29.
[9]高卫峰,三阳,焦合华,等.采用人工蜂群搜索算子的粒子群算法[J].控制与决策,2012,27(6):833-838.
[10]颜丽燕,张桂珠.基于蜂群算法的多维Qo S云计算任务调度[J].计算机工程与科学,2016,38(4):648-655.
[11]Mallipeddi R,Mallipeddi S,Suganthan P N.Ensemble strategies with adaptive evolutionary programming[J].Information Sciences,2010,180(9):1571-1581.
[12]Karaboga D,Gorkemli B.A quick Artificial Bee Colony(qA BC)algorithm and its performance on optimization problems[J].Applied Soft Computing,2014,23(5):227-238.