A quantum-inspired evolutionary algorithm for multi-attribute combinatorial auctions

Due to the recent research advances on quantum computing, ideas from this field have been increasingly used as a source of inspiration for new variants of evolutionary algorithms. In this paper, the QIEA-SSEHC algorithm is proposed for solving multi-attribute combinatorial auction problems in multi-agent systems, characterized by an evolutionary hill-climbing phase, a steady state model and a repair procedure to keep all the individuals feasible. The results are compared to those of NSGA-II, a well-known multi-objective evolutionary algorithm, and convergence and diversity metrics are used to assess the quality of multidimensional solutions.