An Algorithm for Combinatorial Double Auctions Based on Cooperative Coevolution of Particle Swarms
A combinatorial double auction is a type of double-side auction which makes buyers and sellers trade goods more conveniently than multiple combinatorial auctions. However, the winner determination problem (WDP) in combinatorial double auctions poses a challenge due to computation complexity. Particle swarm optimization (PSO) is one of the well-known meta-heuristic approaches to deal with complex optimization problems. Although there are many studies on combinatorial auctions, there is little study on application of PSO approach in combinatorial double auctions. In this paper, we consider combinatorial double auction problem in which there are transaction costs, supply constraints and non-negative surplus constraints. We formulate the WDP of combinatorial double auction problem as an integer programming problem formulation. As standard discrete PSO algorithm suffers from the premature convergence problem, we adopt a cooperative coevolution approach to develop a discrete cooperative coevolving particle swarm optimization (DCCPSO) algorithm that can scale with the problem better. The effectiveness of the proposed algorithm is illustrated by several numerical examples by comparing the results with standard DPSO algorithm.
KeywordsMeta-heuristics Particle swarm Coevolution Combinatorial double auction Integer programming
This paper was supported in part by Ministry of Science and Technology, Taiwan, under Grant MOST-106-2410-H-324-002-MY2.
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