A Multi-Objective Genetic Algorithm for High Dimensionality Optimization Problem and Study of Its Convergence Properties

Pure Pareto genetic algorithms cannot be expected to perform well on problems that involve many competing objectives By introducing preference information among several goals, a multi objective genetic algorithm is proposed, whose character lies in that evolutionary population is preference ranked based on concordance model The algorithm transforms a normal method with which individuals are ranked by Pareto superior relationship Also, it is proven that the new algorithm can guarantee the convergence towards the global optimum under some condition Mathematics parses of typical computational samples and experiments show that it can achieve good convergent performance and speed