Abstract
A typical combinatorial optimization problem, Graph Coloring Problem (GCP), has a wide range of applications in the fields of science and engineering. A cuckoo quantum evolutionary algorithm (CQEA) is proposed for the GCP, which is based on the framework of quantum-inspired evolutionary algorithm. To reduce iterations for the search of the chromatic number, the initial quantum population is generated with 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 GCP. Numerical results show that CQEA has strong exploration and exploitation ability, and is competitive compared with the state-of-the-art heuristic algorithms.
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Notes
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Zero rows are rows of a matrix where all elements are equal to “0”.
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Xu, Y., Chen, Y. (2022). A Cuckoo Quantum Evolutionary Algorithm for the Graph Coloring Problem. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_7
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