求解全局优化问题的改进人工鱼群算法
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摘要
针对人工鱼群算法的不足,提出一种改进的人工鱼群算法NAFAS。该算法对原有觅食行为进行改进,引进双高斯函数与其融合,使在寻优后期人工鱼群能快速逃离局部极值区域,从而提高全局寻优能力。与其它多种智能算法进行仿真测试并比较分析,结果表明,改进的人工鱼群算法搜索速度快、寻优精度高。
Artificial fish swarm algorithm optimizes through the simulation of the fish behaviors,such as preying,swarming,following and moving in the search area,which is an application of the swarm intelligence. It has the advantages of the better global search abilities and the excellent robustness. What's more,the algorithm is easily and simply operated. But it is easy to fall into local optima in the flat area and becomes lower in the later period of algorithm. To overcome the shortages of the artificial fish swarm algorithm,this paper presents an improved artificial fish swarm algorithm which is named NAFAS. In order to enhance the global searching ability,the bimodal Gaussian is integrated into the function of the prey behavior. so the artificial fish can escape from local extreme areas quickly.Compared with some typical evolutionary algorithms,the numerical experiment results show that NAFAS not only has efficient search performance on the optimal precision excellently,but is also an excellent algorithm for solving global optimization problems.
Artificial fish swarm algorithm optimizes through the simulation of the fish behaviors,such as preying,swarming,following and moving in the search area,which is an application of the swarm intelligence. It has the advantages of the better global search abilities and the excellent robustness. What's more,the algorithm is easily and simply operated. But it is easy to fall into local optima in the flat area and becomes lower in the later period of algorithm. To overcome the shortages of the artificial fish swarm algorithm,this paper presents an improved artificial fish swarm algorithm which is named NAFAS. In order to enhance the global searching ability,the bimodal Gaussian is integrated into the function of the prey behavior. so the artificial fish can escape from local extreme areas quickly.Compared with some typical evolutionary algorithms,the numerical experiment results show that NAFAS not only has efficient search performance on the optimal precision excellently,but is also an excellent algorithm for solving global optimization problems.
引文
[1]李宁.粒子群优化算法的理论分析与应用研究[D].武汉:华中科技大学,2006.
[2]黄友锐.智能优化算法及其应用[M].北京:国防工业出版社,2008.
[3]KARABOGA D.A powerful and efficient algorithm for numerical function optimization:artificial bee colony(ABC)algorithm[J].Journal of Global Optimization,2007,39(30):459-471.
[4]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,22(1):32-38.
[5]汪铭炎,袁东风.人工鱼群算法及其应用[M].北京:科学出版社,2012.
[6]李晓磊.一种新型的智能优化方法--人工鱼群算法[D].杭州:浙江大学,2003.
[7]黄华娟,周永权.改进型人工鱼群算法及其复杂函数全局优化算法[J].广西师范大学学报:自然科学版,2008,26(1):194-197.
[8]刘彦君,江铭炎.自适应视野和步长改进的人工鱼群算法[J].计算机工程与应用,2009,45(25):35-37.
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[10]曲良东,何登旭.一种混沌人工鱼群优化算法[J].计算机工程与应用,2010,46(22):40-42.
[11]姚祥龙.人工鱼群与微粒群智能算法及收敛性分析[J].计算机应用研究,2010,27(6):2084-2086.
[12]涂晓媛.人工鱼--计算机动画的人工生命方法[M].北京:清华大学出版社,2001.
[13]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安:西安交通大学出版社,2002.
[14]薛俊杰,王瑛,李浩.一种狼群智能算法及收敛性分析[J].控制与决策,2016,31(12):2131-2139.
[15]吴虎胜,张凤鸣,吴庐山.一种新的群体智能算法:狼群算法[J].系统工程与电子技术,2013,35(11):2430-2438.
[16]宁爱平,张雪英.人工蜂群算法的收敛性分析[J].控制与决策,2013,28(9):1554-1558.
[17]WANG C F,MA MIN,SHEN P P.A new improved bat algorithm for global optimization[J].Mathemathica Applicata,2016,29(3):632-640.
[18]李爱国,覃征,鲍复民,等.粒子群优化算法[J].计算机工程与应用,2002(21):1-3.
[19]蒋珉,柴干,王宏华,等.控制系统计算机仿真[M].北京:电子工业出版社,2012.
[20]黄岚,王康平,周春光,等.基于蚂蚁算法的混合方法求解旅行商问题[J].吉林大学学报:理学版,2002,40(4):369-373.
[21]秦裕缓.论算法的发现(I)--组合优化的基本方法[J].数学杂志,1994,14(3):435-444.
[2]黄友锐.智能优化算法及其应用[M].北京:国防工业出版社,2008.
[3]KARABOGA D.A powerful and efficient algorithm for numerical function optimization:artificial bee colony(ABC)algorithm[J].Journal of Global Optimization,2007,39(30):459-471.
[4]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,22(1):32-38.
[5]汪铭炎,袁东风.人工鱼群算法及其应用[M].北京:科学出版社,2012.
[6]李晓磊.一种新型的智能优化方法--人工鱼群算法[D].杭州:浙江大学,2003.
[7]黄华娟,周永权.改进型人工鱼群算法及其复杂函数全局优化算法[J].广西师范大学学报:自然科学版,2008,26(1):194-197.
[8]刘彦君,江铭炎.自适应视野和步长改进的人工鱼群算法[J].计算机工程与应用,2009,45(25):35-37.
[9]王联国,施秋红.人工鱼群算法的参数分析[J].计算机工程,2010(12):169-172.
[10]曲良东,何登旭.一种混沌人工鱼群优化算法[J].计算机工程与应用,2010,46(22):40-42.
[11]姚祥龙.人工鱼群与微粒群智能算法及收敛性分析[J].计算机应用研究,2010,27(6):2084-2086.
[12]涂晓媛.人工鱼--计算机动画的人工生命方法[M].北京:清华大学出版社,2001.
[13]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安:西安交通大学出版社,2002.
[14]薛俊杰,王瑛,李浩.一种狼群智能算法及收敛性分析[J].控制与决策,2016,31(12):2131-2139.
[15]吴虎胜,张凤鸣,吴庐山.一种新的群体智能算法:狼群算法[J].系统工程与电子技术,2013,35(11):2430-2438.
[16]宁爱平,张雪英.人工蜂群算法的收敛性分析[J].控制与决策,2013,28(9):1554-1558.
[17]WANG C F,MA MIN,SHEN P P.A new improved bat algorithm for global optimization[J].Mathemathica Applicata,2016,29(3):632-640.
[18]李爱国,覃征,鲍复民,等.粒子群优化算法[J].计算机工程与应用,2002(21):1-3.
[19]蒋珉,柴干,王宏华,等.控制系统计算机仿真[M].北京:电子工业出版社,2012.
[20]黄岚,王康平,周春光,等.基于蚂蚁算法的混合方法求解旅行商问题[J].吉林大学学报:理学版,2002,40(4):369-373.
[21]秦裕缓.论算法的发现(I)--组合优化的基本方法[J].数学杂志,1994,14(3):435-444.