Chaos Glowworm Swarm Optimization Algorithm Based on Cloud Model for Face Recognition-Reference-Cited by-同舟云学术

Chaos Glowworm Swarm Optimization Algorithm Based on Cloud Model for Face Recognition

Published:2020-03-25 Issue:12 Volume:34 Page:2056009
ISSN:0218-0014
Container-title:International Journal of Pattern Recognition and Artificial Intelligence
language:en
Short-container-title:Int. J. Patt. Recogn. Artif. Intell.

Author:

Zhou Guangyu¹,Ouyang Aijia²^ORCID,Xu Yuming³^ORCID

Affiliation:

1. College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo, Zhejiang 315175, P. R. China

2. School of Information Engineering, Zunyi Normal University, Zunyi, Guizhou 563006, P. R. China

3. College of Information Science and Engineering, Changsha Normal University, Changsha 410082, Hunan, P. R. China

Abstract

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.

Funder

Natural Science Foundation of Guizhou Province

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218001420560091

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