图像处理过程中遗传算法的应用

遗传算法是将自然选择和遗传机理结合到一起的一种随机搜索和优化的方法。随着科学家对遗传算法进行优化,其在工业领域也得到了很好的应用,此方法受到了国内外专家的一致认可。文章介绍了遗传算法的基本原理和它在运算过程中的一些特点,主要介绍了遗产算法在图像处理过程中的应用,包括图像压缩、恢复、重建和增强技术等方面,针对目前遗传算法在处理图像过程中存在的一些问题得出这种算法的将来发展方向。     

遗传算法理论及其应用研究进展

首先阐述遗传算法的原理和求解问题的一般过程;然后讨论了近年来从遗传算子、控制参数等方面对遗传算法的改进,并对遗传算法在计算机科学与人工智能、自动控制以及组合优化等领域的应用进行陈述;最后评述了遗传算法未来的研究方向和主要研究内容。     

遗传算法研究综述

遗传算法是一种基于生物自然选择与遗传机理的随机搜索与优化方法。近年来，由于遗传算法求解复杂优化问题的巨大潜力及其在工业工程领域的成功应用，这种算法受到了国内外学者的广泛关注。本文介绍了遗传算法的研究现状，描述了它的主要特点和基本原理，概述了它的理论、技术和应用领域，讨论了混合遗传算法和并行遗传算法，指出了遗传算法的研究方向，并对遗传算法的性能作了分析。     

遗传算法中选择策略的分析

遗传算法是一种基于生物自然选择与遗传机理的随机搜索与优化方法。近年来，由于遗传算法求解复杂优化问题的巨大潜力及其在工业工程领域的成功应用，这种算法受到了国内外学者的广泛关注。本文介绍并讨论了最能体现遗传算法“自然选择、适者生存”特点的选择算子的多种操作策略，提出一些可以进一步改进和完善遗传算法选择算子的研究方向，可提高遗传算法的性能，从而扩大遗传算法在各个领域的应用。     

图像识别处理中的遗传算法分析

图像识别处理研究领域,遗传算法在优化计算方面发挥着重要作用,目前已在图像恢复、几何形状识别、图像边缘特征提取和图像分割等方面发挥着重要作用。基于此,以图像识别处理中的遗传算法为研究对象,简单阐述分析了遗传算法概念,讨论了遗传算法的特点,重点探讨分析了图像识别处理中遗传算法的具体应用,以期为相关人士提供参考。     

基于VB的遗传算法软件实现及其应用

遗传算法是一种模拟达尔文理论的自然进化机制的搜索和优化方法，它在搜索优化问题的全局或附近的最优解上具有良好的优势条件，因此它在很多诸如组合优化，图像处理和故障诊断等的领域都得到了应用，本文对遗传算法基本的数学基础理论，主要特点，算法实现的VB编程和实际应用进行了描述。     

基于遗传算法的肝脏CT图像分割算法研究

遗传算法是一种基于生物选择与遗传机制所形成的一种全局优化、随机搜索算法,对处理一些传统搜索算法解决有难度的复杂问题比较适合,具有巨大发展潜力。论文首先介绍了遗传算法的基本原理以及它的主要特点和性质,重点叙述了基于遗传算法的图像分割方法,并通过应用遗传算法选取合适的阈值,进而采用最大熵原则对人体肝脏CT图像进行了分割算法处理,得到图像分割处理的实验结果,并对实验结果进行了分析。     

遗传算法在优化问题中的应用研究进展

分析了传统优化方法的局限性，阐述了遗传算法的基本思想和特点，综述了遗传算法在函数优化领域的主要成果，并指出需要进一步研究的工作。     

基于遗传算法的物流配送中心选址问题研究

本文在选择、交叉、变异等操作方面改进了基本遗传算法,将其应用于物流配送优化问题,进行了有益的尝试和分析。在理论上比较了基本遗传算法和改进遗传算法的性能,给出改进遗传算法在物流配送中心应用的算法。     

遗传算法在物流配送中心选址中的应用

本文在选择、交叉、变异等操作方面改进了基本遗传算法,将其应用于物流配送优化问题,进行了有益的尝试和分析,在理论上比较了基本遗传算法和改进遗传算法的性能,给出改进遗传算法在物流配送中心应用的算法。     

多聚焦图像融合算法

多聚焦图像融合,是将两幅(或多幅)对同一场景的各个目标,聚焦不同的图像融合成一幅清晰的新图像.在多聚焦图像融合中,典型的群智能算法图像融合方法取得了较好的效果,如遗传算法、粒子群算法等.目前,对群智能算法的优化改进,加快图像的融合速度是一个主要的研究方向.     

A hybrid genetic algorithm and particle swarm optimization for multimodal functions

Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. The results of various experimental studies using a suite of 17 multimodal test functions taken from the literature have demonstrated the superiority of the hybrid GA-PSO approach over the other four search techniques in terms of solution quality and convergence rates.     

面向端元提取的粒子群优化遗传算法

现有的粒子群优化(PSO)算法和遗传算法(GA)无法很好地解决高光谱影像端元提取这类离散解空间内的大规模取样优化问题。针对该问题,借鉴凸面几何学理论,利用局部模式粒子群优化的原理改进遗传算法,提出一种面向高光谱影像端元提取的粒子群优化遗传算法(PSOGA)。利用模拟数据和PHI影像对PSOGA算法和GA算法进行实验对比。分析结果证明,PSOGA算法的收敛速度优于GA算法。     

PERFORMANCE COMPARISON OF OPTIMIZATION TECHNIQUES ON ROBUST DIGITAL-IMAGE WATERMARKING,AGAINST ATTACKS

Increasing illegal exploitation and imitation of digital images in the field of image processing has led to the urgent development of copyright protection methods. Digital watermarking has proved to be the most effectivemethod for protecting illegal authentication of data. In this article, we propose a hybrid digital-image watermarking scheme based on computational intelligence paradigms such as a genetic algorithm (GA) and particle swarm optimization (PSO). The watermark image is embedded into the host image using discrete wavelet transform (DWT). During the extraction process, GA, PSO, and the hybrid combination of GA and PSO are applied to improve the robustness and fidelity of the watermarked image by evaluating the fitness function. The perceptual transparency and the robustness of both the watermarked and the extracted images is evaluated by applying filtering attacks, additive noise, rotation, scaling, and JPEG compression attacks to the watermarked image. From the simulation results, the performance of the hybrid particle swarm optimization technique is proved best, based on the computed robustness and transparency measures, as well as the evaluated parameters of elapsed time, computation time, and fitness value. The performance of the proposed scheme was evaluated with a set of 50 textures images taken from online resources of Tampere University of Technology, Finland, and the entire algorithm for different stages was simulated using MATLAB R2008b.     

基于改进初始解的遗传算法的布局设计方法

带性能约束的三维布局问题属于具有很强应用背景的组合优化问题,进行了基于全局的布局求解方法的探索。由于NP完全问题的计算复杂性,使得遗传算法求解问题的全局最优解时效率较低。改进了遗传算法的初始解,对提高算法的效率进行了研究。并以旋转卫星舱布局的简化模型为背景,建立了多目标优化数学模型。实例结果与传统遗传算法以及乘子法的计算结果比较,表明该算法具有较好的求解效率。     

An evolutionary tabu search for cell image segmentation

Many engineering problems can be formulated as optimization problems. It has become more and more important to develop an efficient global optimization technique for solving these problems. In this paper, we propose an evolutionary tabu search (ETS) for cell image segmentation. The advantages of genetic algorithms (GA) and TS algorithms are incorporated into the proposed method. More precisely, we incorporate "the survival of the fittest" from evolutionary algorithms into TS. The method has been applied to the segmentation of several kinds of cell images. The experimental results show that the new algorithm is a practical and effective one for global optimization; it can yield good, near-optimal solutions and has better convergence and robustness than other global optimization approaches.     

An Application on Mobile Devices with Android and IOS Operating Systems Using Google Maps APIs for the Traveling Salesman Problem

Nowadays, the Traveling Salesman Problem (TSP) is one of the most studied combinational optimization problems that researchers study. Although it is easy to define, its solution is hard. Therefore, it is one of the NP-hard problems in the research literature. It can be used to solve real-life problems such as route planning and scheduling, and transportation and logistics applications. In this study, for TSP, an interface that can run on mobile devices using Android and IOS operating systems is developed. Real-world data are used online by the interface. Locations, and the distance between them, are obtained instantly by Google Maps APIs. Genetic (GA) and ant colony optimization (ACO) algorithms are used to solve the TSP. Furthermore, users have also been allowed to conduct trials for different parameter values. The application developed has been tested on two different datasets. The test results show that for the determination of the optimum route, the ACO algorithm is better than the GA. However, when considering the run times, GA works much faster than ACO.     

GENETIC ALGORITHMS: A TOOL FOR MODELLING,SIMULATION, AND OPTIMIZATION OF COMPLEX SYSTEMS

Until very recently genetic algorithms GAs were considered to be the proprietary field of general systems theoreticians and important for esoteric or extremely complex optimization studies. This paper endeavors to show that GA are of great utility in cases where complex systems have to be designed and, therefore, rational choices have to be made. The GA approach is based loosely on the theory of natural evolution, genetic diversity, and searching for beneficial adaptations to a complicated and changing environment. GAs can be viewed as a modelling tool and as a technique for simulation of complex systems represented by communities of interacting units. The representation of units can express characteristics, capabilities, or relatively simple strategies. These units compete and are modified by external operators, so that the overall system adapts to its environment. That environment defines the criterion by which the success in adapting can be measured. Genetic algorithms have been successfully applied to many optimization problems including mathematical function optimization, very large scale integration VLSI chip layout, molecular docking, parameter fitting, scheduling, manufacturing, clustering, machine learning, etc. and are still finding increasing acceptance. Modelling and optimization of a Kanban system from the field of flexible manufacturing systems is discussed in the last section.     

Stepwise-then-intelligent algorithm (STIA) for optimizing remotely sensed image rectification

ABSTRACT

To address the problems of parameter selection and accuracy optimization of models in image rectification, this article first proposes a novel stepwise-then-intelligent algorithm (STIA) for image rectification optimization, which includes the following steps. First, stepwise regression is suggested to simultaneously solve the over-parameterization problem and select the optimum parameters of the polynomial model and rational function model according to different terrains. Second, intelligent algorithms, e.g. the genetic algorithm (GA) and particle swarm optimization (PSO), are proposed to search for better results based on an innovative search range determined by the uncertainty propagation and 3-sigma rule. The experimental results show that the proposed STIA can achieve higher accuracy than conventional methods; and in most cases, the PSO algorithm used in STIA is superior to the GA used in STIA in measures of time and accuracy. Moreover, stepwise-then-PSO algorithm exhibits the best performance of all compared methods, including least squares, stepwise regression, total least squares and partial least squares.     

A double-multiplicative dynamic penalty approach for constrained evolutionary optimization

The present paper proposes a double-multiplicative penalty strategy for constrained optimization by means of genetic algorithms (GAs). The aim of this research is to provide a simple and efficient way of handling constrained optimization problems in the GA framework without the need for tuning the values of penalty factors for any given optimization problem. After a short review on the most popular and effective exterior penalty formulations, the proposed penalty strategy is presented and tested on five different benchmark problems. The obtained results are compared with the best solutions provided in the literature, showing the effectiveness of the proposed approach.