A co-evolutionary algorithm based on mixed mutation strategy for WDP in combinatorial auction

Abstract

To address computational complexity of winner determination in combinatorial auction, a new co-evolutionary algorithms is developed based on combining mixed mutation with self-organization optimization for finding high quality solutions quickly. Mixed mutation strategy can select adaptively mutation operators which are suitable for discrete space to maintain population diversity, self-organization optimization makes the search to jump out of local optima. This paper investigates two combination methods of mixed mutation and self-organization optimization, the results of experiment show the better performance of the second way (MMSEO2) that self-organization optimization is added to mixed mutation strategy set as a pure mutation operator. We compare the proposed algorithm with current well-known approximate algorithms for winner determination problem, and demonstrate that the proposed algorithm MMSEO2 produces competitive results and finds better solutions than other algorithms for large problem sizes.