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Constraint-guided evolutionary algorithm for solving the winner determination problem

Author

Listed:
  • Fernanda Nakano Kazama

    (University of Campinas (UNICAMP))

  • Aluizio Fausto Ribeiro Araujo

    (Universidade Federal de Pernambuco (UFPE))

  • Paulo Barros Correia

    (University of Campinas (UNICAMP))

  • Elaine Guerrero-Peña

    (Universidade Federal de Pernambuco (UFPE))

Abstract

Combinatorial Auctions (CAs) allow the participants to bid on a bundle of items and can result in more cost-effective deals than traditional auctions if the goods are complementary. However, solving the Winner Determination Problem (WDP) in CAs is an NP-hard problem. Since Evolutionary Algorithms (EAs) can find good solutions in polynomial time within a huge search space, the use of EAs has become quite suitable for solving this type of problem. In this paper, we introduce a new Constraint-Guided Evolutionary Algorithm (CGEA) for the WDP. It employs a penalty component to represent each constraint in the fitness function and introduces new variation operators that consider each package value and each type of violated constraint to induce the generation of feasible solutions. CGEA also presents a survivor selection operator that maintains the exploration versus exploitation balance in the evolutionary process. The performance of CGEA is compared with that of three other evolutionary algorithms to solve a WDP in a Combinatorial Reverse Auction (CRA) of electricity generation and transmission line assets. Each of the algorithms compared employs different methods to deal with constraints. They are tested and compared on several problem instances. The results show that CGEA is competitive and results in better performance in most cases.

Suggested Citation

  • Fernanda Nakano Kazama & Aluizio Fausto Ribeiro Araujo & Paulo Barros Correia & Elaine Guerrero-Peña, 2021. "Constraint-guided evolutionary algorithm for solving the winner determination problem," Journal of Heuristics, Springer, vol. 27(6), pages 1111-1150, December.
    • Handle: RePEc:spr:joheur:v:27:y:2021:i:6:d:10.1007_s10732-021-09485-x
    DOI: 10.1007/s10732-021-09485-x
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    References listed on IDEAS

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