A Hybrid Parallel Genetic Algorithm for the Winner Determination Problem in Combinatorial Auctions

2011 ◽  
pp. 65-78
Author(s):  
Dalila Boughaci ◽  
Louiza Slaouti ◽  
Kahina Achour
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Auctions ◽  
Parallel Genetic Algorithm ◽  
Winner Determination Problem ◽  
Winner Determination

scholarly journals A Tabu Search Method for the Multi-objective Winner Determination Problem of Combinatorial Auctions

2020 ◽  
Vol Volume 14 ◽  
Keyword(s):  
Tabu Search ◽  
Combinatorial Auctions ◽  
Search Method ◽  
Metaheuristic Algorithm ◽  
Winner Determination Problem ◽  
Winner Determination ◽  
Multi Objective ◽  
Tabu Search Method ◽  
Single Objective

We are interested by the problem of combinatorial auctions in which multiple items are sold and bidders submit bids on packages. First, we present a multi-objective formulation for a combinatorial auctions problem extending the existing single-objective models. Indeed, the bids may concern several specifications of the item, involving not only its price, but also its quality, delivery conditions, delivery deadlines, the risk of not being paid after a bid has been accepted and so on. The seller expresses his preferences upon the suggested items and the buyers are in competition with all the specified attributes done by the seller. Second, we develop and implement a metaheuristic algorithm based on a tabu search method.

A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions

2021 ◽  
Author(s):  
Abhishek Ray ◽  
Mario Ventresca ◽  
Karthik Kannan
Keyword(s):  
Exact Algorithms ◽  
Combinatorial Auctions ◽  
Ant Colony ◽  
Superior Performance ◽  
Winner Determination Problem ◽  
Winner Determination ◽  
Anytime Algorithm ◽  
Open Test ◽  
Valid Solution ◽  
Graph Pruning

Iterative combinatorial auctions are known to resolve bidder preference elicitation problems. However, winner determination is a known key bottleneck that has prevented widespread adoption of such auctions, and adding a time-bound to winner determination further complicates the mechanism. As a result, heuristic-based methods have enjoyed an increase in applicability. We add to the growing body of work in heuristic-based winner determination by proposing an ant colony metaheuristic–based anytime algorithm that produces optimal or near-optimal winner determination results within specified time. Our proposed algorithm resolves the speed versus accuracy problem and displays superior performance compared with 20 past state-of-the-art heuristics and two exact algorithms, for 94 open test auction instances that display a wide variety in bid-bundle composition. Furthermore, we contribute to the literature in two predominant ways: first, we represent the winner determination problem as one of finding the maximum weighted path on a directed cyclic graph; second, we improve upon existing ant colony heuristic–based exploration methods by implementing randomized pheromone updating and randomized graph pruning. Finally, to aid auction designers, we implement the anytime property of the algorithm, which allows auctioneers to stop the algorithm and return a valid solution to the winner determination problem even if it is interrupted before computation ends.

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