Research On Artificial Bee Colony Algorithm For Constrained Optimization

Optimization problems are widespread in the various optimization fields of modern society such as natural science,engineering,etc.,these practical optimization problems is generally not only single objective optimization but also objective function with many complex constraints.These problems are called the constrained optimization problems(COPs).Therefore,solving these optimization problems becomes very difficult.The traditional optimization method has become impotent in the face of optimization problems which has some complex characteristics such as discontinuities and non-differentiable.Artificial Bee Colony algorithm as an intelligent optimization method is a new approach of search optimization inspired by the foraging behavior of honeybee.Due to its' advantages,such as easy to implement,strong robustness,and less control parameters,ABC algorithm attracts extensive attention of researchers and scholars and has been successfully applied to solve a various of optimization problems.In this paper,ABC algorithm is studied for COPs,the constrained ABC algorithm and the corresponding constraint handling method is presented to enhance the capacity of the original ABC algorithm for solving practical optimization problems respectively.The main research result of this paper as follows:(1)An improved artificial bee colony algorithm based on search balanced method is proposed for more efficiently solving the COPs.For constraint handling,the method based on ? constraint handling make full use of the valuable information carried by individual that is located within the inside and outside of the ? range and cannot survive into the population of the next generation to increase the diversity of the population.For search algorithm,according to the situation of population,the employed bees and the onlooker bees select different search strategies to improve the search efficiency of the algorithm.The new algorithm is extensively tested with some benchmark test function and three engineering problems in the domain of constrained optimization and the results are compared with other constrained optimization algorithms,indicating that the algorithm is highly competitive.(2)Due to the variability of the characteristic of different COPs,optimization algorithms with no single constraint handling technique,performs consistently over a range of problems.This thesis proposes an artificial bee colony algorithm based on hybrid method for solving the constrained optimization problem.The algorithm adopts two-group co-evolution to overcome the shortcomings of the two constraint handling method,and give full play to the advantages of two kinds of constraint handling methods.The experimental results of the new algorithm.are compared with other constrained optimization algorithms.The experimental results show the effectiveness of the algorithm.