摘要
针对基本粒子群算法易陷入局部最优和过早收敛的缺陷,提出权重因子自适应的粒子群算法,并对部分粒子进行Morlet变异操作,由此得到改进粒子群优化算法.将该算法和模糊熵相结合并用于图像分割,利用改进粒子群优化算法来搜索使模糊熵最大的参数值,得到模糊参数的最优组合,进而确定图像的分割阈值.通过与其他两种粒子群算法的分割结果进行比较,该算法取得了令人满意的分割结果,且算法运算时间较小,满足煤尘浓度实时精确测量的要求.
Basic particle swarm optimization（PSO） can not get good optimization performance,because it is easy to get stuck into local optima.Therefore,an algorithm named improved PSO which combines proposed inertia adaptive PSO with partial particles Morlet mutation is proposed.The proposed algorithm and fuzzy entropy are applied to image segmentation,and improved PSO is used to explore fuzzy parameters of maximum fuzzy entropy,which gets the optimum fuzzy parameter combination,then obtains the segmentation threshold.By comparing the proposed algorithm with other two algorithms,the experiment results show that the proposed algorithm has the capability of good segmentation performance and low time cost,which can be use to real time and precision measure coal dust image.
出处
《控制与决策》
EI
CSCD
北大核心
2011年第2期276-279,284,共5页
Control and Decision
基金
山东省自然科学基金项目(Z2006G06)
关键词
粒子群
Morlet变异
权重因子自适应
模糊熵
图像分割
particle swarm optimization
Morlet mutation
inertia adaptive
fuzzy entropy
image segmentation
