引入高斯分布缩放因子的人工蜂群算法  

Artificial bee colony algorithm with Gaussian distribution scaling factor

作　　者：王守金[1] 程小桐 宋晓宇[1] WANG Shou-jin;CHENG Xiao-tong;SONG Xiao-yu(Information and Control Engineering Faculty,Shenyang Jianzhu University,Shenyang 110168,China)

机构地区：[1]沈阳建筑大学信息与控制工程学院

出　　处：《计算机工程与设计》2019年第9期2507-2512,2536,共7页Computer Engineering and Design

基　　金：辽宁省自然科学基金项目(2017054767)

摘　　要：为解决原始人工蜂群算法收敛速度慢、开发性不足的问题,在保证其探索性的基础上增强开发性,提出引入高斯分布缩放因子的人工蜂群算法。在引领蜂阶段采用基于邻域的最优解学习搜索策略,增加种群多样性;在跟随蜂阶段采用能够自适应调节搜索步长的三角变异搜索策略,修改其食物源的选择方式;两个搜索方程中引入高斯分布缩放因子,利用搜索方程特性及通过实验设定高斯参数,实现算法在探索和开发之间的平衡。采用10个不同特性的标准测试函数进行仿真实验,验证了改进后的算法在搜索质量、收敛速度、鲁棒性等方面的优越性。To solve the low convergence speed and insufficient development of the original artificial colony algorithm,an artificial bee colony algorithm with Gaussian distribution scaling factor was proposed to strengthen exploration on the basis of enhancing the exploitation.A searching strategy learnt from the optimal solution based on neighborhoods was used in the employed bee phase,which increased the diversity of population.A searching equation based on the trigonometric mutation was adopted,which adjusted searching step adaptively and modified the way of choosing foods source.The scaling factor of two searching equations obeyed Gaussian distribution,which realized the balance between exploration and exploitation through using the characteristic of two equations and setting Gaussian parameters by experiment.The results show the advantage of improved algorithm in searc-hing quality,the speed of convergence and robustness by using 10 standard test functions with different characteristics.

关 键 词：人工蜂群算法 群体智能 搜索策略 自适应 收敛速度 高斯分布 缩放因子 函数优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]