2021/08/19 by Yongjian Xu, Yu Chen, Xu, Yongjian +1 · 1 citation
Decision Sciences · #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Scheduling and Timetabling Solutions
paper · pdf · doi:10.48550/arxiv.2108.08691
openalex publication_date 2021/08/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.