Paper Info
Title
A hybrid quantum-inspired genetic algorithm for flow shop scheduling
Abstract
This paper is the first to propose a hybrid quantum-inspired genetic algorithm (HQGA) for flow shop scheduling problems. In the HQGA, Q-bit based representation is employed for exploration in discrete 0-1 hyperspace by using updating operator of quantum gate as well as genetic operators of Q-bit. Then, the Q-bit representation is converted to random key representation. Furthermore, job permutation is formed according to the random key to construct scheduling solution. Moreover, as a supplementary search, a permutation-based genetic algorithm is applied after the solutions are constructed. The HQGA can be viewed as a fusion of micro-space based search (Q-bit based search) and macro-space based search (permutation based search). Simulation results and comparisons based on benchmarks demonstrate the effectiveness of the HQGA. The search quality of HQGA is much better than that of the pure classic GA, pure QGA and famous NEH heuristic.
Year
DOI
Venue
2005
10.1007/11538356_66
ICIC (2)
Keywords
Field
DocType
q-bit representation,flow shop scheduling problem,genetic operator,permutation-based genetic algorithm,search quality,random key representation,hybrid quantum-inspired genetic algorithm,pure qga,job permutation,supplementary search,flow shop scheduling,genetic algorithm,quantum gate
Heuristic,Computer science,Scheduling (computing),Flow shop scheduling,Permutation,Beam search,Quantum computer,Algorithm,Best-first search,Genetic algorithm
Conference
Volume
ISSN
ISBN
3645
0302-9743
3-540-28227-0
Citations 
PageRank 
References 
25
1.44
7
Authors
4
Name
Order
Citations
PageRank
Ling Wang12745165.98
Hao Wu214318.69
Fang Tang3655.28
Dazhong Zheng421223.38