The study of dynamic pricing for traditional clothing helps business managers better balance the dual goals of sales profit and cultural heritage.In this paper,for the situation of unknown demand distribution of tradi-tional clothing,a multi-objective dynamic pricing model of traditional clothing based on Markov Decision Process(MDP)is constructed by using a deep reinforcement learning method and a multi-objective particle swarm algo-rithm based on Deep Deterministic Policy Gradient(DDPG)is proposed for solving the multi-objective dynamic pri-cing problem of traditional clothing.By comparing the Pareto optimal solutions obtained iteratively by the multi-ob-jective particle swarm algorithm(MOPSO),the multi-objective hybrid particle swarm algorithm(MOHPSO),and the multi-objective particle swarm algorithm based on DDPG(MOPSO-DDPG),it is verified that MOPSO-DDPG has a stronger advantage in terms of extensiveness and convergence effect.
关键词
传统服饰/DDPG/多目标粒子群算法/Pareto
Key words
traditional clothing/DDPG/multi-objective particle swarm optimization/Pareto
维普
万方数据