基于非欧几何权向量产生策略的分解多目标优化算法

doi:10.11896/jsjkx.230900007

Abstract

Abstract: 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 complex Pareto fronts,decomposition-based algorithms show poor diversity in population and the performance deteriorates.To address 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 estimating 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,compared with MOEA/D,NSGA-III and AR-MOEA algorithms,the proposed algorithm has significant performance in solving multi-objective optimization problems.

Key words: Decomposition, Multi objective, Weight vector, Non-dominated front, Non-Euclidean geometry

CLC Number:

TP391

SUN Liangxu, LI Linlin, LIU Guoli. Decomposition Multi-objective Optimizaiton Algorithm with Weight Vector Generation StrategyBased on Non-Euclidean Geometry[J].Computer Science, 2024, 51(11): 280-291.

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Decomposition Multi-objective Optimizaiton Algorithm with Weight Vector Generation StrategyBased on Non-Euclidean Geometry

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