Many problems in combinatorial optimization are NP-Hard. This has forced researchers to explore meta-heuristic techniques for dealing with this class of complex problems and finding an acceptable solution in reasonable time.
In this chapter, we are interested in the winner determination problem (WDP) in combinatorial auction (CA). CA is an auction that allocates a set of many goods to bidders in the presence of substitutes and complements. The winner determination problem is a complex problem. It is the problem of finding winning bids that maximize the auctioneers revenue under the constraint that each good can be allocated to at most one bidder.
This chapter presents a computational experience regarding four well-known meta-heuristics (stochastic local search, tabu search, genetic algorithms andmemetic algorithms) for solving the winner determination problem (WDP). The purpose of this study is to evaluatethe performance of each one of the different techniques to solve the WDP in combinatorial auctions. The different methods are evaluated on various benchmark problems, and compared with the hybrid simulated annealing (SAGII) and Casanova. The computational experiments show that in general the metaheuristic approaches provide competitive results and find good quality solutions.