  Based on the framework of the quantum-inspired evolutionary algorithm, a
cuckoo quantum evolutionary algorithm (CQEA) is proposed for solving the graph
coloring problem (GCP). To reduce iterations for the search of the chromatic
number, the initial quantum population is generated by random initialization
assisted by inheritance. Moreover, improvement of global exploration is
achieved by incorporating the cuckoo search strategy, and a local search
operation, as well as a perturbance strategy, is developed to enhance its
performance on GCPs. Numerical results demonstrate that CQEA operates with
strong exploration and exploitation abilities, and is competitive to the
compared state-of-the-art heuristic algorithms.
