A novel hybrid quantum-inspired evolutionary algorithm for permutation flow-shop scheduling

Abstract

This paper mainly studies the application of a novel Hybrid Quantum-Inspired Evolutionary Algorithm (HQEA) called DV_HQEA (HQEA based on Differential evolution and Variable neighborhood search) in permutation flow-shop scheduling problem (PFSSP). In this new method, a simple representation method to determine job sequence based on Q -bit’s probability amplitude information is proposed for PFSSP firstly. Then the quantum chromosomes are encoded and decoded by using the quantum rotating angle rather than the two probability amplitude strings which are widely adopted in the conventional QEAs. Also, we merge the advantages of Differential Evolution (DE) strategy, Variable Neighborhood Search (VNS) and QEA, and propose the new DV_HQEA. By adopting the DE to perform the updating of quantum gate and VNS to perform the local search, we can obtain high performance. Simulation results and comparisons with other algorithms such as NEH heuristic and QEA based on famous benchmarks show the effectiveness of the proposed DV_HQEA.