改进樽海鞘群优化K-means算法的图像分割

目的 针对樽海鞘群算法寻优精度低、易陷入到局部最优,以及K-means算法进行图像分割容易被初始聚类中心干扰等缺点,提出改进樽海鞘群优化K-means算法的图像分割。方法 首先利用Circle映射来对樽海鞘种群进行初始化;其次引入莱维飞行到领导者和追随者位置更新公式中,使得樽海鞘种群的多样性得到提高,克服算法陷入到局部最优。最后,对改进樽海鞘群算法先采用8个基准函数进行性能测试;再将改进樽海鞘群算法优化K-means进行图像分割。结果 改进算法在寻优精度、稳定性、收敛速度以及跳出局部最优的本领得到了提高。同时,改进樽海鞘群优化K-means算法进行图像分割,有效地提高了图像分割质量。结论 改进算法改善了原始樽海鞘群算法的寻优精度低、易陷入到局部最优的缺点,很好地优化了K-means算法对图像进行准确分割,在图像分割领域具有一定的参考意义。     

基于生化反应速率的图像阈值分割算法

提出了一种改进的DNA遗传算法,以解决遗传算法用于图像分割时收敛速度慢、易早熟的缺点。利用碱基互补的DNA编码方式增加种群多样性,防止陷入局部极值;设计了基于DNA分子操作的置换自适应交叉算子和密码子变异算子,从而提高遗传算法的搜索能力,有效加快了算法的收敛速度和效率,并将此算法用于寻找二维Arimoto熵的最佳阈值,最后对图像进行分割。普通图像和医学图像的分割结果验证了改进DNA遗传算法用于图像分割的有效性。     

基于纹理特征的钢丝绳图像分割方法

针对复杂背景下钢丝绳图像难以准确分割的问题,提出一种新的基于纹理特征的图像分割方法.首先,采用局部二进制模式(Local Binary Pattern,LBP)特征直方图的一阶熵、二阶熵作为LBP特征的统计测度,降低LBP特征的维数.同时选用边缘密度作为纹理描述的特征之一,弥补LBP算子提取纹理特征不足,抗干扰能力差的缺点.然后以上述纹理特征构成特征矢量,采用模糊C-均值(Fuzzy C-Mean,FCM聚类算法进行聚类分割.在实验中,对比了该算法与灰度共生矩阵、传统LBP算子在钢丝绳图像分割中的效果.结果表明,该算法可以有效地对钢丝绳图像进行纹理分割,并能取得良好的边界定位效果,性能优于另外两种算法.     

模拟退火与模糊C-均值聚类相结合的图像分割算法

模糊C-均值(FCM)聚类算法是一种结合无监督聚类和模糊集合概念的图像分割技术,比较有效,但存在着受初始聚类中心和隶属度矩阵影响,可能收敛到局部极小的缺点.将模拟退火算法(SA)与模糊C-均值聚类算法相结合,在合理选择冷却进度表的基础上,依据模糊C-均值聚类算法建立模拟退火算法的目标函数,实现了基于模拟退火的模糊C-均值聚类图像分割算法.实验表明,该方法具有搜索全局最优解的能力,因而可得到很好的图像分割结果.     

基于免疫算法进行图像分割的应用和研究

图像分割是图像处理的关键技术,是将图像分成一些有意义的区域,然后对这些区域进行描述。免疫系统是高度并行的分布式信息处理系统,受到免疫系统的启发,免疫算法是模拟人体免疫过程而产生的新型算法,具有独特的自学习自组织性和高速的并行计算特点,免疫算法的思路也可灵活的应用于其它算法中,本文正是以免疫算法所具有独特的自组织性出发,讨论了其应用于图像分割领域的发展和应用。     

基于SLIC超像素分割的图分割算法

图像分割是对图像进行分析和理解的关键步骤,是计算机视觉的基本技术之一.计算复杂度是评判一个图像分割算法好坏的重要标准,因此降低算法的计算复杂度是当前图像分割领域的主要任务之一.本文提出了一种基于SLIC超像素分割的图像分割方法.该方法利用SLIC算法生成超像素,通过构造相应的相似性矩阵,有效降低了Ncut分割算法的计算复杂度,大幅度缩短了Ncut算法的运行时间.由于SLIC超像素分割算法的准确性与高效性,在进行三类自然图像分割实验时,本文提出的方法无论在分割效果,还是在运行时间上,都要明显优于Ncut分割方法及它的改良算法.     

一种精确的医学细胞图像边缘检测法

细胞边缘检测是进行细胞形态分析的基础,其检测结果直接影响病情分析和诊断的结果。传统的边缘检测算子由于受噪声影响比较大,无法检测细胞可靠的边缘位置,因而不宜应用于细胞形态分析。提出一种用迭代算法求图像分割最佳阈值和运用数学形态学的腐蚀算法实现轮廓提取相结合的细胞图像边缘精确检测算法,并给出仿真实例。与传统的边缘检测算子Laplacian-Gauss算子、Sobel算子相比较,该算法具有检测精度高和抗干扰能力强的优点。     

基于模拟退火并行遗传算法的Otsu双阈值医学图像分割

模拟退火和并行遗传算法是两种较好的改进进化算法性能的方法。将这两种思想有机地结合起来,利用遗传算法能全局寻优的优势和模拟退火算法的爬山性能,提出了一种基于模拟退火并行遗传算法的Otsu双阈值医学图像分割算法。在该算法中,进化在多个不同的子群中并行进行,利用模拟退火算法的爬山性能,避免单种群进化过程中出现的过早收敛现象,提高整个算法的收敛速度。实验证明,这种新的图像分割算法与并行遗传算法相比,不仅能够对图像进行准确的分割,而且具有更强的精确性和稳定性。其收敛速度明显比并行遗传算法的Otsu双阈值医学图像分割快。     

基于MATLAB的图像分割算法分析

作为数字图像处理的关键技术,图像分割在图像分析系统中发挥了不可忽视的作用。随着科学技术的不断进步,有很多不同的算法被应用到图像分割技术中,但是最常用的分析方法包括基于阈值的分割方法、基于区域的分割方法、基于特定理论的分割方法以及基于边缘的分割方法。该文主要利用MATLAB软件对基于边缘的分割算法和基于阈值图像的分割算法进行仿真分析。阈值分割技术的关键在于确定阈值,利用Otsu算法能够自动选取阈值,对图像进行分割;在边缘检测算法中,对4种算子(Roberts、Sobel、Prewitt和LOG)的分割结果进行图像分析可以得出,LOG检测算子得出的边缘检测结果最好。     

分水岭变换和统计区域合并的图像分割算法研究

提出了一种基于分水岭变换和统计区域合并的图像分割方法.该方法综合利用高斯低通滤波、分水岭变换和统计区域合并,先对原始图像提取分割标记,然后利用Meyer分水岭变换对标记分水岭进行分割,最后利用概率统计的方法对过分割区域进行合并.该算法通过调节尺度参数可以实现由粗到细(coarse-to-fine)的分割.实验结果表明,这种简单可行的算法在分割噪声图像时依然有良好的效果,具有较强的鲁棒性.     

A memetic algorithm with iterated local search for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is a difficult vehicle routing problem, where given an undirected graph, the objective is to minimize the total cost of all vehicle tours that serve all required edges under vehicle capacity constraints. In this paper, a memetic algorithm with iterated local search (MAILS) is proposed to solve this problem. The proposed MAILS incorporates a new crossover operator, i.e., the longest common substring crossover (LCSX), an iterated local search (ILS) and a perturbation mechanism into the framework of the memetic algorithm (MA). The proposed MAILS is evaluated on the CARP benchmark instances and computational results show that the MAILS is very competitive.     

MOEA/D-SQA: a multi-objective memetic algorithm based on decomposition

A multi-objective memetic algorithm based on decomposition is proposed in this article, in which a simplified quadratic approximation (SQA) is employed as a local search operator for enhancing the performance of a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The SQA is used for a fast local search and the MOEA/D is used as the global optimizer. The multi-objective memetic algorithm based on decomposition, i.e. a hybrid of the MOEA/D with the SQA (MOEA/D-SQA), is designed to balance local versus global search strategies so as to obtain a set of diverse non-dominated solutions as quickly as possible. The emphasis of this article is placed on demonstrating how this local search scheme can improve the performance of MOEA/D for multi-objective optimization. MOEA/D-SQA has been tested on a wide set of benchmark problems with complicated Pareto set shapes. Experimental results indicate that the proposed approach performs better than MOEA/D. In addition, the results obtained are very competitive when comparing MOEA/D-SQA with other state-of-the-art techniques.     

A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility

In existing scheduling models, the flexible job-shop scheduling problem mainly considers machine flexibility. However, human factor is also an important element existing in real production that is often neglected theoretically. In this paper, we originally probe into a multi-objective flexible job-shop scheduling problem with worker flexibility (MO-FJSPW). A non-linear integer programming model is presented for the problem. Correspondingly, a memetic algorithm (MA) is designed to solve the proposed MO-FJSPW whose objective is to minimise the maximum completion time, the maximum workload of machines and the total workload of all machines. A well-designed chromosome encoding/decoding method is proposed and the adaptive genetic operators are selected by experimental studies. An elimination process is executed to eliminate the repeated individuals in population. Moreover, a local search is incorporated into the non-dominated sorting genetic algorithm II. In experimental phase, the crossover operator and elimination operator in MA are examined firstly. Afterwards, some extensive comparisons are carried out between MA and some other multi-objective algorithms. The simulation results show that the MA performs better for the proposed MO-FJSPW than other algorithms.     

A smart repair embedded memetic algorithm for 2D shape matching problems

Shape representation plays a major role in any shape optimization exercise. The ability to identify a shape with good performance is dependent on both the flexibility of the shape representation scheme and the efficiency of the optimization algorithm. In this article, a memetic algorithm is presented for 2D shape matching problems. The shape is represented using B-splines, in which the control points representing the shape are repaired and subsequently evolved within the optimization framework. The underlying memetic algorithm is a multi-feature hybrid that combines the strength of a real coded genetic algorithm, differential evolution and a local search. The efficiency of the proposed algorithm is illustrated using three test problems, wherein the shapes were identified using a mere 5000 function evaluations. Extension of the approach to deal with problems of unknown shape complexity is also presented in the article.     

基于改进免疫遗传优化蚁群算法的移动机器人路径寻优研究

针对移动机器人路径规划中使用蚁群算法(ACO)易陷入局部最优和收敛速度慢的问题,提出了一种适用于机器人静态路径寻优的改进免疫遗传优化蚁群算法(IMGAC)。该算法可以根据实际情况自动调整变异概率和变异方式,以及自动调节个体免疫位的长度,将通过改进的变异算子和免疫算子嵌入蚁群算法来提高全局寻优能力与收敛速度。仿真及实验表明:相比于经典ACO算法以及最大最小蚂蚁系统,IMGAC算法收敛速度更快,全局寻优能力更强。利用该算法寻找移动机器人最优路径,提高了静态路径寻优的效果和效率。     

A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called ‘antivirus’) to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.     

A weight-based multiobjective immune algorithm: WBMOIA

A novel immune algorithm is suggested for finding Pareto-optimal solutions to multiobjective optimization problems based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. In the proposed algorithm, a randomly weighted sum of multiple objectives is used as a fitness function, and a local search algorithm is incorporated to facilitate the exploitation of the search space. Specifically, a new truncation algorithm with similar individuals (TASI) is proposed to preserve the diversity of the population. Also, a new selection operator is presented to create the new population based on TASI. Simulation results on seven standard problems (ZDT2, ZDT6, DEB, VNT, BNH, OSY and KIT) show that the proposed algorithm is able to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the vector immune algorithm and the elitist non-dominated sorting genetic system.     

特殊工艺约束下并行机多目标调度问题

针对并行机多目标调度问题的特点,提出了一个多目标调度模型,设计了一个基于向量组编码的遗传算法,并对其初始种群、选择、交叉和变异等方法进行了研究。此算法的编码方法能有效地反映实际调度方案,并引入免疫算子,保证了种群的多样性和种群的质量,加快了收敛速度。仿真结果表明,此算法是有效的,优于没引入免疫算子的遗传算法。     

考虑废物包装时间的车辆回收路径规划方法

针对考虑废物包装时间的车辆回收路径规划问题,建立问题数学模型,提出禁忌搜索算法与模因算法求解该问题,并与爬山算法、遗传算法进行对比.模因算法是爬山算法和遗传算法的结合.实验结果表明:在解的质量方面,禁忌搜索算法与模因算法所求出的解的质量要远远好于另外两种算法,但在运行时间上,禁忌搜索、爬山算法与遗传算法要远优于模因算法.     

A rule-centric memetic algorithm to minimize the number of tardy jobs in the job shop

This paper addresses the job shop-scheduling problem with minimizing the number of tardy jobs as the objective. This problem is usually treated as a job-sequencing problem, and the permutation-based representation of solutions was commonly used in the existing search-based approaches. In this paper, the flaw of the permutation-based representation is discussed, and a rule-centric concept is proposed to deal with it. A memetic algorithm is then developed to realize the proposed idea by tailored genome encoding/decoding schemes and a local search procedure. Two benchmark approaches, a multi-start hill-climbing approach and a simulated annealing approach, are compared in the experiments. The results show that the proposed approach significantly outperforms the benchmarks.