Graph coloring with a distributed hybrid quantum annealing algorithm

Authors:
Olawale Titiloye;Alan Crispin
Affiliations:
School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, UK;School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, UK
Venue:
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Year:
2011

Citing 7
Cited 1

Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993

Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
Simulated Annealing: Searching for an Optimal Temperature Schedule

SIAM Journal on Optimization
A graph coloring heuristic using partial solutions and a reactive tabu scheme

Computers and Operations Research
An adaptive memory algorithm for the k-coloring problem

Discrete Applied Mathematics
A Metaheuristic Approach for the Vertex Coloring Problem

INFORMS Journal on Computing
An evolutionary approach with diversity guarantee and well-informed grouping recombination for graph coloring

Computers and Operations Research
Note: Quantum annealing of the graph coloring problem

Discrete Optimization

Improving the extraction and expansion method for large graph coloring

Discrete Applied Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Quantum simulated annealing is analogous to a population of agents cooperating to optimize a shared cost function defined as the total energy between them. A hybridization of quantum annealing with mainstream evolutionary techniques is introduced to obtain an effective solver for the graph coloring problem. The state of the art is advanced by the description of a highly scalable distributed version of the algorithm. Most practical simulated annealing algorithms require the reduction of a control parameter over time to achieve convergence. The algorithm presented is able to keep all its parameters fixed at their initial value throughout the annealing schedule, and still achieve convergence to a global optimum in reasonable time. Competitive results are obtained on challenging problems from the standard DIMACS benchmarks. Furthermore, for some of the graphs, the distributed hybrid quantum annealing algorithm finds better results than those of any known algorithm.