Decomposition Multi-objective Optimizaiton Algorithm with Weight Vector Generation Strategy Based on Non-Euclidean Geometry
With the increase of the number of objectives,mult-objective problems(MOPs)are more and more difficult to solve.Decomposition-based multi-objective evolutionary algorithms show better performance.However,when solving MOPs with com-plex Pareto fronts,decomposition-based algorithms show poor diversity in population and the performance deteriorates.To ad-dress these issues,this paper proposes a decomposition multi-objective optimization algorithm with weight vector generation strategy based on non-Euclidean geometry.By fitting the non-dominated frontier in the non-Euclidean geometric space and estima-ting the parameters,the normal statistical sampling of the target variable of the non-dominated solution is used to generate the weight vector,so as to guide the evolution direction of the population and maintain the diversity of the population.Meanwhile,the neighborhood of sub-problems can be rebuilt periodically,to improve the efficiency of the co-evolution of decomposition algorithm and improve the performance of the algorithm.Experiment results based on the MaF benchmark test problems show that,com-pared with MOEA/D,NSGA-Ⅲ and AR-MOEA algorithms,the proposed algorithm has significant performance in solving multi-objective optimization problems.
万方数据
维普
