Hybrid Orthogonal Genetic Algorithm for Global Optimization

In order to improve convergent speed and search precision of orthogonal genetic algorithm,a local search scheme is introduced into orthogonal genetic algorithm and a new clustering local search operator is proposed.The orthogonal operator is used to generate an initial population of points that are scattered uniformly over the feasible solution space,so that it can maintain diversity of the initial population.The orthogonal operator is used for a global search to guarantee the global convergence,a new clustering local search operator is adopted to make a local search in order to improve the convergence speed and the search precision.The new approach is tested on seven benchmark functions with high dimensions.Compared with other algorithms,the new algorithm has better search precision,convergent speed and capacity of global search.