| 一种狼群智能算法及收敛性分析 |
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| 引用本文： | 薛俊杰,王瑛,李浩,肖吉阳.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. |
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| 作者姓名： | 薛俊杰 王瑛 李浩 肖吉阳 |
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| 作者单位： | 空军工程大学装备管理与安全工程学院,西安710051,空军工程大学装备管理与安全工程学院,西安710051,空军工程大学装备管理与安全工程学院,西安710051;空军预警学院预警情报系,武汉430019,空军工程大学装备管理与安全工程学院,西安710051 |
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| 基金项目： | 国家自然科学基金项目(71601183) |
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| 摘 要： | 针对狼群算法求解复杂函数时容易陷入局部极值、计算耗费大、学习能力差等局限性, 提出一种狼群智能算法. 首先, 通过构建智能猎杀行为提高算法自适应学习能力, 降低算法的计算耗费, 构建双高斯函数更新法以增强算法全局搜索能力; 然后, 运用马尔科夫过程证明狼群智能算法的收敛性; 最后, 对多种典型测试函数进行仿真实验并与多种智能算法进行对比分析. 实验结果表明, 所提出算法具有全局收敛性强、计算耗费低、寻优精度高等优势.
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| 关 键 词： | 狼群智能算法 智能猎杀 双高斯函数 马尔科夫过程 |
| 收稿时间： | 2015/9/28 0:00:00 |
| 修稿时间： | 2015/9/28 0:00:00 |
A smart wolf pack algorithm and its convergence analysis |
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| XUE Jun-jie,WANG Ying,LI Hao and XIAO Ji-yang.A smart wolf pack algorithm and its convergence analysis[J].Control and Decision,2016,31(12):2131-2139. |
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| Authors: | XUE Jun-jie WANG Ying LI Hao and XIAO Ji-yang |
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| Affiliation: | College of Equipment Management and Safety Engineering,Air Force Engineering University,Xi''an 710051,China,College of Equipment Management and Safety Engineering,Air Force Engineering University,Xi''an 710051,China,College of Equipment Management and Safety Engineering,Air Force Engineering University,Xi''an 710051,China;Department of Early Warning Information,Air Force Early Warning Academy,Wuhan430019,China. and College of Equipment Management and Safety Engineering,Air Force Engineering University,Xi''an 710051,China |
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| Abstract: | In order to improve the searching performance(global optimal, computational cost, learning ability etc.) of wolf pack algorithm in solving complex functions, a novel algorithm---Smart wolf pack algorithm(SWPA) is proposed. First of all, intelligent hunting is proposed to improve the adaptive learning ability and reduce the computational cost. Then the bimodal Gaussian regeneration method is applied to enhance the global searching ability. The, Markov process is used to prove the convergence of SWPA. Finally, compared with some typical evolutionary algorithms, simulation on several Benchmark functions is analyzed. Results show that the SWPA has excellent searching performance on the global optimization ability, the convergence rate and the optimal precision. |
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