Winner Determination in Combinatorial Auctions using Hybrid Ant Colony Optimization and Multi-Neighborhood Local Search

A combinatorial auction is an auction where the bidders have the choice to bid on bundles of items. The Winner Determination Problem (WDP) in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under constraint, where each item can be allocated to at most one bidder. WDP is known as an NP-hard problem with practical applications like electronic commerce, production management, games theory, and resource allocation in multi-agent systems. This has motivated the quest for efficient approximate algorithms in terms of both the solution quality and computational time. This paper proposes a hybrid Ant Colony Optimization with a novel Multi-Neighborhood Local Search (ACO-MNLS) algorithm for solving WDP in combinatorial auctions. Our proposed MNLS algorithm uses the fact that using various neighborhoods in local search can generate different local optima for WDP and that the global optima of WDP is a local optima for a given neighborhood. Therefore, the proposed MNLS algorithm simultaneously explores a set of three different neighborhoods to get different local optima and to escape from the local optima. The comparisons between ACO-MNLS, Genetic Algorithm (GA), Memetic Algorithm (MA), Stochastic Local Search (SLS), and Tabu Search (TS) on various benchmark problems confirm the efficiency of the ACO-MNLS algorithm in terms of both the solution quality and computational time.