中文摘要：

为了克服多属性反向拍卖中偏好信息提取困难、提取成本高的影响，提出一种迭代多属性拍卖机制，支持买卖双方增量式的按需偏好确定和揭示，引入协同演化计算方法作为卖方的学习工具和投标策略工具．在买方采取坦诚策略的前提下，协同演化计算方法为卖方提供的策略是其事后的近似纳什均衡策略，且拍卖机制以近似有效率的交易结束．实例分析和实验结果验证了该机制的有效性．

英文摘要：

Aimed at the problem that preference elicitation is time-consuming and costly in multi-attribute auctions, a novel iterative multi-attribute auction mechanism for reverse auction settings with one buyer and many sellers is proposed. The auctions support incremental preference elicitation and revelation for the buyer and the sellers. Co-evolutionary computation method is incorporated into the mechanism to support economic learning and strategies for the sellers. The strategy provided by it is in ex-post Nash equilibrium for sellers, assumed that the buyer takes a truthful strategy. Experimental results show that the co-evolutionary computation based iterative multi-attribute auction is a practical and nearly efficient mechanism.