Towards Better Approximation of Winner Determination for Combinatorial Auctions with Large Number of Bids

We propose new approximate algorithms for combinatorial auctions with massively large number of (more than 100,000) bids. In this paper, we focus on a more practical approximated algorithm in the context of revenue maximization. We propose a hill-climbing greedy algorithm, a SA-like random search algorithm, and their enhancement for searching multiple key parameter values. The experimental results demonstrate that our algorithms perform approximately 0.997 optimality compared with the optimal solutions and better than previously presented approximated algorithms. We also demonstrate that our algorithms are a kind of anytime algorithm that bring better results in shorter computational time that can be applied to large and dynamic electronic markets.