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

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.