Reinforcement learning guided multi-objective differential evolutionary algorithm for product change paths
SONG Xian-fang
YANG Yang
ZHANG Yong
ZHENG Rui-zhao
Abstract：Due to the complex interrelationships between components,the propagation of product design change effect is inevitable.To reduce the risk associated with product design changes,this paper proposes a multi-objective differential evolution algorithm guided by reinforcement learning to optimize the impact of changes on the product performance,economic cost,and change duration.Firstly,a complex product network model is established to reveal the propagation mechanism of component changes.Then,the change propagation intensity is introduced to indirectly evaluate the impact of component changes on the product performance.Meanwhile,considering the economic cost and change duration of design changes,a multi-objective model for optimizing design change propagation paths is established.Furthermore,utilizing dual deep Q-networks to assist populations in selecting appropriate evolutionary strategies at different stages,introducing a reinforcement learning-guided differential evolution algorithm,abbreviated as DDQN-DE,and using the above-mentioned algorithm to determine the optimal propagation path for product design changes.Finally,taking the design change problem of a TV product from sky worth company as an example and comparing with existing algorithms,the validity of the proposed algorithm is verified through experiments.
Keywords：multiobjective optimizationdesign changedifferential evolutionreinforcement learning
Publication Date：2025-01-27
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 109-117 )