A Quantum-Inspired Adaptive Tabu Search Algorithm with Inverse Learning
QIAN Jie1,2, ZHENG Jian-guo2
1. School of Information, Hubei University of Automotive Technology, Shiyan, Hubei 442002, China;
2. Intelligent Systems Research Center, Donghua University, Shanghai 200051, China
Abstract：In order to enhance the local optimization capability of quantum-inspired evolutionary algorithm (QEA),a novel QEA incorporating inverse learning mode is proposed based on adaptive tabu search.In this algorithm,the neighborhood structure and tabu tenure can be adjusted dynamically casing quantum entanglement states,so that the conflict between intensification and diversification is well solved.At the same time,a novel quantum updating mode named inverse learning is designed to help individuals get out of inferior region.Therefore,better balance between exploration and exploitation can be achieved to escape from a local optimum.Experiment results show that local optimization ability has been advanced effectively through the proposed algorithm.
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2013, Vol. 41 
