摘要
针对人工蜂群(ABC)算法不易跳出局部最优解的缺点,提出了多交互式人工蜂群(MIABC)算法。该算法在基本人工蜂群算法的基础上引入随机邻域搜索策略,结合跨维搜索策略,且改进蜜蜂越限处理方式,使得算法搜索方式多样化,从而使得算法搜索更具跳跃性,不易陷入局部最优解,同时,对其进行收敛性分析和性能测试。在五种经典基准测试函数和时间复杂度实验上的仿真结果表明,相对于标准人工蜂群算法和基本粒子群优化(PSO)算法,该算法在1E-2精度下收敛速度提高了约30%和65%,搜索精度更优,且在高维求解问题方面有明显优势。
Aiming at the shortcomings of Artificial Bee Colony （ABC） algorithm, which is not easy to jump out of the local optimal value, a Multiple Interactive Artificial Bee Colony （MIABC） algorithm was proposed. The proposed algorithm was based on the basic ABC algorithm, involved the random neighborhood search strategy and the cross-dimensional search strategy, and improved the treatment when bees exceed the limit, so the search way of the algorithm became various, the algorithm itself had stronger bound and it＇s hard to trap in the local optimal value. Meanwhile, the convergence analysis and performance test were carried out. The simulation result based on five kinds of classic benchmark functions and experimental results for time complexity show that comparing with the standard ABC algorithm and basic Particle Swarm Optimization （PSO）, this proposed method has faster convergence speed which is increased by about 30% and 65% at 1E -2 accuracy and better search precision, besides, it has significant advantages in solving high dimensional problems.
出处
《计算机应用》
CSCD
北大核心
2017年第3期760-765,共6页
journal of Computer Applications
基金
上海市教委科研创新项目(13YZ140)~~
关键词
人工蜂群算法
跨维度搜索策略
随机邻域搜索策略
搜索精度
收敛性分析
Artificial Bee Colony （ABC） algorithm
cross-dimensional search strategy
stochastic neighborhood search strategy
search precision
convergence analysis
