Improving performance of evolutionary algorithm by remodeling evolutionary population

Reduction of population diversity leads to premature convergence,which limits search capability and computational efficiency of evolutionary algorithm.To deal with premature convergence,the evolutionary population is updated with elite solutions and new created random solutions periodically during evolutionary process.Adding elite solutions means inheriting results got by anterior evolutionary process from the beginning and adding new solutions created randomly improves population diversity.For large search scale,population is remodeled many times in the whole evolutionary process according to the search scale.To test solution quality and computational efficiency,the proposed remodeling population strategy is applied to symbiotic evolutionary algorithm for dealing with a flexible job-shop scheduling problem.Compared with the widely used traditional evolutionary algorithm,improved algorithm shows better performance for different search scale no matter whether the problem is large or not.The most important is it presents a solution for dealing with premature convergence,which deeply limits performance of evolutionary algorithm.With the remodeling population strategy,the applying depth and width of evolutionary algorithms will be improved.