Matchmaking in Multi-attribute Auctions using a Genetic Algorithm and a Particle Swarm Approach

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 383)

Abstract

An electronic market platform usually requires buyers and sellers to exchange offers-to-buy and offers-to-sell. The goal of this exchange is to reach an agreement on the suitability of closing transactions between buyers and sellers. This paper investigates multi-attribute auctions, and in particular the matchmaking of multiple buyers and sellers based on five attributes. The proposed approaches are based on a Genetic Algorithm (GA) and a Particle Swarm Optimization (PSO) approach to match buyers with sellers based on five attributes as closely as possible. Our approaches are compared with an optimal assignment algorithm called the Munkres algorithm, as well as with a simple random approach. Measurements are performed to quantify the overall match score and the execution time. Both, the GA as well as the PSO approach show good performance, as even though not being optimal algorithms, they yield a high match score when matching the buyers with the sellers. Furthermore, both algorithms take less time to execute than the Munkres algorithm, and therefore, are very attractive for matchmaking in the electronic market place, especially in cases where large numbers of buyers and sellers need to be matched efficiently.

Keywords

Genetic Algorithm Particle Swarm Optimization Particle Swarm Optimization Algorithm Genetic Algorithm Algorithm Random Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceNorth Dakota State UniversityFargoU.S.A.
  2. 2.Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada

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