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Intell."],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:p>To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures.<\/jats:p>","DOI":"10.1142\/s0218001420560091","type":"journal-article","created":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T08:30:26Z","timestamp":1580459426000},"page":"2056009","source":"Crossref","is-referenced-by-count":2,"title":["Chaos Glowworm Swarm Optimization Algorithm Based on Cloud Model for Face Recognition"],"prefix":"10.1142","volume":"34","author":[{"given":"Guangyu","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo, Zhejiang 315175, P. R. 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