一种求解高维约束优化问题的人工蜂群算法

New high-dimensional constrained optimization algorithm based on artificial bee colony

针对求解高维约束优化中算法的收敛速度和解的精度不高的缺点,提出一种改进的人工蜂群约束优化算法。该算法在初始化种群和侦察蜂探寻新蜜源时采用了正交实验设计方法,并在采蜜蜂搜索时使用了改进的高斯分布估计,跟随蜂按照采蜜蜂的适应值大小选择一个采蜜蜂,在其蜜源领域内采用差异算法搜索新的蜜源;在处理约束条件时采用自适应优劣解比较方法。最后通过13个标准的Benchmark测试函数进行仿真实验,结果表明该算法在处理高维约束优化问题时具有较好的收敛性和稳定性。

About convergence rate and solution precision are not high in high-dimensional constrained optimization problem（COP）,this paper proposed an improved ABC optimization algorithm.Firstly,it used the orthogonal experimental design algorithm to generate initial population and discover a new food source for the scout.Secondly,employed bees used Gaussian distribution estimate algorithm（GDEA） to search,according to fitness value,onlooker bees selected one employed bees and search new nectar source in a self-adaptive differential search algorithm.Thirdly,processed constrained condition by self-adaptive fit and unfit quality solution comparison.At last tested this algorithm on 13 standard benchmark functions,and the experimental result show algorithm has some advantages in convergence velocity,solution precision,and stabilization.