New high-dimensional constrained optimization algorithm based on artificial bee colony
一种求解高维约束优化问题的人工蜂群算法*

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.