School of Mathematics and Statistic, Hubei Engineering University, Xiaogan, Hubei 432000, China.
Comput Intell Neurosci. 2013;2013:231916. doi: 10.1155/2013/231916. Epub 2013 Jul 15.
Differential evolution algorithm (DE) is one of the novel stochastic optimization methods. It has a better performance in the problem of the color image quantization, but it is difficult to set the parameters of DE for users. This paper proposes a color image quantization algorithm based on self-adaptive DE. In the proposed algorithm, a self-adaptive mechanic is used to automatically adjust the parameters of DE during the evolution, and a mixed mechanic of DE and K-means is applied to strengthen the local search. The numerical experimental results, on a set of commonly used test images, show that the proposed algorithm is a practicable quantization method and is more competitive than K-means and particle swarm algorithm (PSO) for the color image quantization.
差分进化算法(DE)是一种新颖的随机优化方法。它在彩色图像量化问题上具有更好的性能,但用户很难为 DE 设置参数。本文提出了一种基于自适应 DE 的彩色图像量化算法。在提出的算法中,自适应机制用于在进化过程中自动调整 DE 的参数,并且 DE 和 K-均值的混合机制用于加强局部搜索。在一组常用的测试图像上的数值实验结果表明,所提出的算法是一种可行的量化方法,并且在彩色图像量化方面比 K-均值和粒子群算法(PSO)更具竞争力。