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Computational Intelligence and Neuroscience
Volume 2016, Article ID 2450431, 12 pages
Research Article

Multiobjective Image Color Quantization Algorithm Based on Self-Adaptive Hybrid Differential Evolution

1School of Information and Mathematics, Yangtze University, Jingzhou, Hubei 434023, China
2School of Software, East China Jiaotong University, Nanchang 330013, China

Received 19 July 2016; Revised 24 August 2016; Accepted 4 September 2016

Academic Editor: Manuel Graña

Copyright © 2016 Zhongbo Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In recent years, some researchers considered image color quantization as a single-objective problem and applied heuristic algorithms to solve it. This paper establishes a multiobjective image color quantization model with intracluster distance and intercluster separation as its objectives. Inspired by a multipopulation idea, a multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution (MoDE-CIQ) is then proposed to solve this model. Two numerical experiments on four common test images are conducted to analyze the effectiveness and competitiveness of the multiobjective model and the proposed algorithm.