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通过放松竞买人对拍卖物品的替代性或互补性的一致性认识假设,在组合拍卖机制设计的基础上建立了基于竞买人报价的组合拍卖模型.为了高效率地获得物品的最优分配方式,运用particle swarm optimization(PSO)算法模拟物品分配方式的寻优过程,在此基础上构建了基于PSO算法的组合拍卖模型.在Swarm仿真平台上对基于PSO算法的组合拍卖模型进行设计与实现,并通过一个具体的组合拍卖算例进行仿真验证,结果分析表明基于PSO算法的组合拍卖模型能够有效地解决多个物品的分配问题,并能实现卖主收益的最大化.学习能力参数分析表明,与自我学习能力相比,社会学习能力对卖主收益的优化更加重要.本文的研究结果对组合拍卖的理论研究和实际应用具有一定的借鉴价值.
By relaxing the concurrence assumption of alternative or complementarity of bidders to auction items, a bidding-based portfolio auction model based on bidders’ bidding mechanism is established based on the design of bidding auction mechanism.In order to get the optimal distribution of items efficiently, Particle swarm optimization (PSO) algorithm is used to simulate the optimization process of goods distribution, and then a composite auction model based on PSO algorithm is constructed.On the Swarm simulation platform, the design and implementation of portfolio auction model based on PSO The simulation results show that the PSO-based portfolio auction model can effectively solve the distribution problem of multiple items and maximize the profit of the seller.Study on learning ability parameters shows that, with the self Compared with the learning ability, the social learning ability is more important to the seller’s profit optimization.The research results in this paper have certain reference value for the theoretical research and practical application of portfolio auctions.