Balancing Profit and Cultural Heritage: Multi-Objective Dynamic Pricing for Hanfu Using Deep Deterministic Policy Gradient

Dynamic pricing is a critical strategy in e-commerce, enabling merchants to optimize sales profit while adapting to varying market conditions. However, existing approaches often fall short in balancing commercial objectives with the preservation of cultural heritage, particularly in niche markets like Hanfu apparel. To address this challenge, we developed a dynamic pricing simulation environment based on a Markov Decision Process (MDP) and introduced a novel multi-objective hybrid particle swarm optimization algorithm combined with Deep Deterministic Policy Gradient (DDPG), referred to as MOHPSO-DDPG. By applying principal component analysis (PCA) to consumer preference data and constructing utility functions and Logit choice models, we accurately simulated consumer behavior. MOHPSO-DDPG, Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Hybrid Particle Swarm Optimization (MOHPSO) were each deployed to interact with the environment to explore the Pareto front of pricing decisions. Experimental results demonstrate that MOHPSO-DDPG significantly outperforms other algorithms in terms of solution diversity and convergence efficiency. After 3,000 iterations, its Generation Distance (GD) reached 0.023 and the Diversity Metric $\Delta $ was 0.594, whereas GD and Diversity Metric $\Delta $ values remained larger for MOPSO and MOHPSO. Moreover, MOHPSO-DDPG continued to maintain a leading position in later iterations, underscoring its superiority in identifying comprehensive and near-optimal Pareto fronts. These findings validate that MOHPSO-DDPG provides an efficient multi-objective dynamic pricing decision-making framework for the Hanfu market, effectively balancing profit maximization with the demands of cultural heritage preservation.