计算机科学 ›› 2016, Vol. 43 ›› Issue (2): 311-315.doi: 10.11896/j.issn.1002-137X.2016.02.065
张新明,涂强,尹欣欣,冯梦清
ZHANG Xin-ming, TU Qiang, YIN Xin-xin and FENG Meng-qing
摘要： 针对标准粒子群优化(Particle Swarm Optimization,PSO)算法在优化选择多阈值时易陷入局部最优等问题,提出一种嵌入趋化算子的PSO算法。首先针对标准PSO算法具有较强的全局寻优能力但局部搜索能力较差,而细菌觅食优化(Bacterial Foraging Optimization,BFO)算法具有较强的局部搜索能力的特点,将BFO算法中具有较强局部搜索能力的趋化算子嵌入到PSO算法中,形成一种嵌入趋化算子的PSO算法(Chemotaxis Operator embedded PSO,COPSO),以此优势互补,使COPSO算法既有较强的全局搜索能力,又有较强的局部搜索能力。最后将COPSO算法用于最大熵多阈值图像分割中,得到最佳阈值向量。实验结果表明:与标准的PSO、BFO和GA算法相比,该算法具有更好的优化效果和更短的寻优时间。
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