三级领导式的快速自适应狼群优化算法
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摘要
为提高狼群算法的收敛速度,在此提出了一种称为三级领导式和微粒进化方程的自适应狼群算法,人为地把灰狼分成两类,领导层三只灰狼:如αβ和δ,剩下的为猛狼w。在游走搜索阶段随机设定一个猎物位置,利用狼群与猎物之间的距离来指导游走搜索猎物;在召唤阶段,利用三个领导层灰狼作为头狼来引导猛狼向猎物靠近,避免了传统狼群算法只有一只头狼引导整个狼群就容易陷入局部最优的情况;在围攻猎物阶段利用惯性因子来表示以往奔袭的经验、学习因子与随机数之间的乘积来表示猛狼自身经验的认识与总结、迭代影响因子来表示整体狼群经验的认识与调整,综合起来狼群粒子奔袭速度加快收敛速度和跳出局部最优,从而找到真实的整体最优值。本次选取的8个测试函数对应的对比性实验结果表明:该方法较为精确地实现寻找到了测试函数的最优值且较早地快速收敛到最优解,在后期也平稳收敛到真实的最优值,该算法适用于多维多波峰函数求极值问题。
In order to improve the convergence speed of wolf swarm algorithm, an adaptive wolf swarm algorithm called three-level leadership and particle evolution equation is proposed, man-made grey wolves are divided into two categories:the leadership grey wolves, such as alpha, beta and delta, and the rest are the fierce wolf. In the wandering search stage, a prey position is set, and the distance between wolves and prey is used to guide the wandering search. In the calling stage,three best gray wolf particles are used to guide the wolves to approach the prey, avoiding the traditional situation that only one wolf is easy to fall into local optimum. In the siege stage, inertia factor is used to express the past experience of running. The product of learning factor and random number represents the knowledge and summary of the experience of the wolves themselves, and the iteration influence factor represents the knowledge and adjustment of the experience of the wolves as a whole. The speed of attack of wolf swarm particles accelerate the convergence speed and jump out of the local optimum, so as to find the real global optimum. The comparative experimental results of the eight test functions selected in this paper show that the method finds the optimal value of the test function more accurately, and converges to the optimal value earlier and faster, and to the real optimal value smoothly in the later period. The algorithm is applicable to multi-dimension and multi-peak function extremum problem with unknown search space.
In order to improve the convergence speed of wolf swarm algorithm, an adaptive wolf swarm algorithm called three-level leadership and particle evolution equation is proposed, man-made grey wolves are divided into two categories:the leadership grey wolves, such as alpha, beta and delta, and the rest are the fierce wolf. In the wandering search stage, a prey position is set, and the distance between wolves and prey is used to guide the wandering search. In the calling stage,three best gray wolf particles are used to guide the wolves to approach the prey, avoiding the traditional situation that only one wolf is easy to fall into local optimum. In the siege stage, inertia factor is used to express the past experience of running. The product of learning factor and random number represents the knowledge and summary of the experience of the wolves themselves, and the iteration influence factor represents the knowledge and adjustment of the experience of the wolves as a whole. The speed of attack of wolf swarm particles accelerate the convergence speed and jump out of the local optimum, so as to find the real global optimum. The comparative experimental results of the eight test functions selected in this paper show that the method finds the optimal value of the test function more accurately, and converges to the optimal value earlier and faster, and to the real optimal value smoothly in the later period. The algorithm is applicable to multi-dimension and multi-peak function extremum problem with unknown search space.
引文
[1]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.
[2] Mirjalili S,Mirjalili S M,Lewis A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(3):46-61.
[3] Tang Qin,Shen Yi,Hu Chengyu,et al.Swarm intelligence:based cooperation optimization of multi-modal functions[J].Cognitive Computation,2013,5(1):48-55.
[4]袁罗,葛洪伟,姜道银.基于健康度的自适应过滤粒子群算法[J].计算机科学与探索,2018,12(2):332-340.
[5] Jensi R,Jiji G W. An enhanced particle swarm optimization with levy flight for global optimization[J].Applied Soft Computing,2016,43(C):248-261.
[6]黄宇达,王迤冉,牛四杰.基于混沌和自适应搜索策略的GSO算法分析与优化[J].计算机工程与应用,2019,55(3):147-153.
[7]郑文萍,李晋玉,王杰.基于遗传算法的蛋白质复合物识别算法[J].计算机科学与探索,2018,12(5):794-803.
[8]曹爽,安建成.改进狼群优化算法的Otsu图像分割法[J].微电子学与计算机,2017,34(10):16-21.
[9]李常洪,赵伟栋.基于狼群算法优化的BP神经网络[J].科技创新与生产力,2016(1):56-58.
[10]刘永兰,李为民,吴虎胜,等.基于狼群算法的无人机航迹规划[J].系统仿真学报,2015,27(8):1838-1843.
[11]吴虎胜,张凤鸣,战仁军,等.求解0-1背包问题的二进制狼群算法[J].系统工程与电子技术,2014,36(8):1660-1667.
[12]李斌,陈爱斌,周国雄,等.基于Cell-DEVS的森林灭火资源调度狼群优化算法[J].计算机应用,2018,38(5):1494-1499.
[13]王建群,贾洋洋,肖庆元.狼群算法在水电站水库优化调度中的应用[J].水利水电科技进展,2015,35(3):1-4.
[14]吴海涛,孙以泽,孟婥.粒子群优化模糊控制器在光伏发电系统最大功率跟踪中的应用[J].中国电机工程学报,2011,31(6):52-57.
[15]郭立婷.基于自适应和变游走方向的改进狼群算法[J].浙江大学学报(理学版),2018,45(3):284-293.
[16] Muangkote N,Sunat K,Chiewchanwattana S.An improved grey wolf optimizer for training q-Gaussian radial basis functional-link nets[C]//Computer Science&Engineering Conference,2014.
[17]徐达宇,Liu Renping.基于改进自组织临界优化的元启发式灰狼优化算法[J].计算机应用,2016,36(6):1588-1593.
[18]吴建辉,章兢,李仁发,等.多子种群微粒群免疫算法及其在函数优化中应用[J].计算机研究与发展,2012,49(9):1883-1898.
[19] Yu Shiwei,Wei Yiming,Zhu Kejun.Hybrid optimization algorithms based on particle swarm optimization and genetic algorithm[J].Systems Engineering&Electronics,2011,8(8):265-274.
[20]於世为,魏一鸣,诸克军.基于粒子群-遗传的混合优化算法[J].系统工程与电子技术,2011,33(7):1647-1652.
[21]张禹,刘群,刘慧芳,等.改进粒子群优化的无刷直流电机模糊控制[J].组合机床与自动化加工技术,2017(10):101-104.
[22]陈超,宣士斌,雷红轩.基于狼群算法与二维最大熵的图像分割[J].计算机工程,2018(1):233-237.
[23]段海滨,张祥银,徐春芳.仿生智能计算[M].北京:科学出版社,2011.
[24]王晓东.计算机算法设计与分析[M].北京:电子工业出版社,2017.
[2] Mirjalili S,Mirjalili S M,Lewis A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(3):46-61.
[3] Tang Qin,Shen Yi,Hu Chengyu,et al.Swarm intelligence:based cooperation optimization of multi-modal functions[J].Cognitive Computation,2013,5(1):48-55.
[4]袁罗,葛洪伟,姜道银.基于健康度的自适应过滤粒子群算法[J].计算机科学与探索,2018,12(2):332-340.
[5] Jensi R,Jiji G W. An enhanced particle swarm optimization with levy flight for global optimization[J].Applied Soft Computing,2016,43(C):248-261.
[6]黄宇达,王迤冉,牛四杰.基于混沌和自适应搜索策略的GSO算法分析与优化[J].计算机工程与应用,2019,55(3):147-153.
[7]郑文萍,李晋玉,王杰.基于遗传算法的蛋白质复合物识别算法[J].计算机科学与探索,2018,12(5):794-803.
[8]曹爽,安建成.改进狼群优化算法的Otsu图像分割法[J].微电子学与计算机,2017,34(10):16-21.
[9]李常洪,赵伟栋.基于狼群算法优化的BP神经网络[J].科技创新与生产力,2016(1):56-58.
[10]刘永兰,李为民,吴虎胜,等.基于狼群算法的无人机航迹规划[J].系统仿真学报,2015,27(8):1838-1843.
[11]吴虎胜,张凤鸣,战仁军,等.求解0-1背包问题的二进制狼群算法[J].系统工程与电子技术,2014,36(8):1660-1667.
[12]李斌,陈爱斌,周国雄,等.基于Cell-DEVS的森林灭火资源调度狼群优化算法[J].计算机应用,2018,38(5):1494-1499.
[13]王建群,贾洋洋,肖庆元.狼群算法在水电站水库优化调度中的应用[J].水利水电科技进展,2015,35(3):1-4.
[14]吴海涛,孙以泽,孟婥.粒子群优化模糊控制器在光伏发电系统最大功率跟踪中的应用[J].中国电机工程学报,2011,31(6):52-57.
[15]郭立婷.基于自适应和变游走方向的改进狼群算法[J].浙江大学学报(理学版),2018,45(3):284-293.
[16] Muangkote N,Sunat K,Chiewchanwattana S.An improved grey wolf optimizer for training q-Gaussian radial basis functional-link nets[C]//Computer Science&Engineering Conference,2014.
[17]徐达宇,Liu Renping.基于改进自组织临界优化的元启发式灰狼优化算法[J].计算机应用,2016,36(6):1588-1593.
[18]吴建辉,章兢,李仁发,等.多子种群微粒群免疫算法及其在函数优化中应用[J].计算机研究与发展,2012,49(9):1883-1898.
[19] Yu Shiwei,Wei Yiming,Zhu Kejun.Hybrid optimization algorithms based on particle swarm optimization and genetic algorithm[J].Systems Engineering&Electronics,2011,8(8):265-274.
[20]於世为,魏一鸣,诸克军.基于粒子群-遗传的混合优化算法[J].系统工程与电子技术,2011,33(7):1647-1652.
[21]张禹,刘群,刘慧芳,等.改进粒子群优化的无刷直流电机模糊控制[J].组合机床与自动化加工技术,2017(10):101-104.
[22]陈超,宣士斌,雷红轩.基于狼群算法与二维最大熵的图像分割[J].计算机工程,2018(1):233-237.
[23]段海滨,张祥银,徐春芳.仿生智能计算[M].北京:科学出版社,2011.
[24]王晓东.计算机算法设计与分析[M].北京:电子工业出版社,2017.