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.
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.
出处
《控制与决策》
EI
CSCD
北大核心
2016年第12期2131-2139,共9页
Control and Decision
基金
国家自然科学基金项目(71601183)
关键词
狼群智能算法
智能猎杀
双高斯函数
马尔科夫过程
smart wolf pack algorithm
intelligent hunting
bimodal Gaussian
Markov process
