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.