One Kind Of Hybrid Genetic Algorithm Combined With Traditional Optimization

Genetic Algorithm (GA) is a new evolutionary algorithm. The algorithm is simple, easy to implement and has strong global optimization capability, a wider range of applications.Traditional optimization algorithm can take full advantage of the information of the problems provided by the neighborhood knowledge, in accordance with the certain principles to find the next iteration from an initial point in the search space, the search process is targeted, rapid convergence, and has advantages of strong local Optimization.In this paper, I design a kind of hybrid genetic algorithm base on traditional optimization algorithm. Major work summarized as follows:Firstly, because of the strong randomness, standard genetic algorithm easily produce shortcomings,such as premature phenomenon,a local maximum,the poor local optimization and late evolutionary convergence is slow. In light of these problem this paper combine the genetic algorithm and the traditional optimization algorithm,A kind of hybrid genetic algorithms (HGA) based on the traditional optimization is proposed; Secondly, this paper complete the theoretical analysis and numerical experiments for the convergence of the algorithm, Through the relevant test function, the results of numerical experiments demonstrate satisfactory optimal performance and the effectiveness of the algorithm; finally the kind of hybrid genetic algorithm applied to the unconstrained and constrained optimization problem.