人工蜂群(Artificial Bee Colony,ABC)算法是一种模仿蜂群寻找蜜源的新型算法,因具有参数简单、灵活性强等优点而被广泛用于解决工程问题.但该算法在早熟、收敛速度慢和个体越界等缺点.为此,提出一种自扰动人工蜂群算法(Novel Artificial Bee Algorithm with Adaptive Disturbance,IGABC).该算法采用轴对称策略处理蜂群中的越界个体,提高了算法的搜索效率.通过改进全局搜索方程的结构,同时加入带阈值的线性递增策略,提出一种全新的自适应搜索方程.自适应搜索方程提高了算法的收敛精度并加快了速度.为了获得更好的全局最优解,提出一种自扰动方法对全局最优解进行扰动.选取18个基准测试函数以及近4年提出的6个改进ABC算法进行对比实验,结果表明,该算法在收敛速度和精度上均有较大的优势,尤其在处理Rosenbrock等很难寻优的复杂函数时,收敛精度提高了16个数量级.%As a new type of algorithm,artificial bee colony simulates the bee behaviors to find food.Since its simple parameters and flexibility,ABC is widely used to solve engineering problems.But the premature convergence and cross-border are disadvantages of ABC.To solve these problems,a novel ABC algorithm with adaptive disturbance(IGABC) was proposed in this paper.This improved algorithm adopted symmetry axis strategy to deal with the cross-border individuals,so the search efficiency is improved.A novel global self-adaptive search equation was proposed in this paper.The new search equation improves the structure of original global search equation,and adds linear increasing strategy with threshold.The search method for onlooker bees and employed bees improves the convergence precision and speed.IGABC algorithm designs a novel method on the base of global adaptive disturbance.The simulation results on 18 benchmark functions show that IGABC algorithm enhances the exploitation capacity,and the convergence speed and accuracy have made great progress,contrasting with other six improved ABC algorithms,which were proposed in the last two years.Especially when the test function is Rosenbrock,which is very difficult to find optimum solution,the convergence precision is increased by 16 orders of magnitude.
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