共查询到19条相似文献,搜索用时 218 毫秒
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汪献忠 《电子工业专用设备》2007,36(12):66-67
针对复杂函数的最优化问题,提出了一种改进的适应度函数设计方法,并将这种适应度函数参与遗传算法对Rosenbrock函数进行了测试,仿真结果表明这种改进的适应度函数在进化的初期能提高算法的优化性能。 相似文献
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针对传统遗传算法中适应度函数把最短路径作为主要遗传因素而未考虑转角及多余路径点造成的时间过多与搜索效率低的情况,文章提出一种改进遗传算法的路径规划方案,把适应度函数选取在满足最短路径的基础上加入角度控制和其余路径信息融合得到新的适应度函数。首先采用栅格法建立实验模型,然后在满足最短路径,路径可行及转弯少的情况下融合成一个新的适应度函数,并其增加插入算子与删除算子进行路径优化得到最优解。最后通过MATLAB实验进行验证,结果表明:在路径规划问题上基于该改进的适应度函数的遗传算法优于基本遗传算法,证明了方案是有效的。 相似文献
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针对传统人工PCI规划全局性差和耗时耗力的问题,提出一种基于遗传算法的PCI分配方法(GA-PCIAS),该方法考虑了PCI规划不冲突、不混淆的原则,并且通过将干扰矩阵引入适应度函数可降低规划区域内的干扰程度,某密集市区的仿真实验证明,基站规模较小时,该算法相对于PCI顺序规划方案可有效降低规划区域的干扰程度. 相似文献
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基于克隆选择的移动机器人路径规划 总被引:1,自引:0,他引:1
提出了基于克隆选择的移动机器人全局路径规划方法,该方法包括环境建模、抗体设计、适应度函数构建,选择策略和免疫算子的定义,在适应度函数构建时既考虑路径距离又考虑与障碍物的相交程度,同时根据机器人路径规划问题设计了变异算子、插入算子和删除算子,该方法与遗传算法相比规划时间更短、规划出的路径质量更高,利用MATLAB仿真验证了该方法的有效性. 相似文献
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3维电场分量对传感器输出信号的耦合干扰是影响3维电场探测准确性的关键问题。为了减小耦合干扰,实现3维电场的准确测量,该文提出一种基于遗传算法的3维电场传感器解耦标定方法。区别于求逆矩阵运算的传统标定方法,该方法利用遗传算法并行全局搜索的特点,通过设置适应度函数和遗传算子获得最优化解耦标定矩阵,避免了复杂的矩阵求逆计算,减小了计算误差。该文设计了一种可实现传感器与电场矢量成任意夹角的测试标定装置,建立了电场理论值计算模型。对基于矩阵求逆计算和基于遗传算法的两种解耦标定方法进行了对比实验,实验结果表明,基于遗传算法的3维电场传感器解耦标定方法与传统标定方法相比,有效地消除了电场分量对传感器的耦合干扰,提高了标定精度,实现了3维电场的准确测量。 相似文献
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提出了一种改进的适应度函数确定方法,算法采用易操作的二进制编码。通过改变适应度函数中的待定参数,有效提高了搜索效率。采用遗传算法对基站天线的方向图赋形,其结果优于同种条件下用Woodward法得到的结果。结合工程实践,考察了本文遗传算法的解的稳定性。 相似文献
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当视频在传输过程中发生不可纠正的差错时,就要用差错掩盖的方法对丢失或差错的块进行掩盖。本文提出了一种基于模糊聚类和遗传算法的视频差错掩盖算法,首先,在确定适应度函数时,采用了一种适应人眼视觉系统(HVS: Human Visual System)的视频评价函数作为目标函数,其次,通过模糊聚类的方法同时综合多特征量得到差错块的相似块得到初始种群,然后利用GA的三个步骤:复制\遗传\交叉进行迭代地运算,直到最后得到满足条件的差错块的掩盖块。实验结果表明,其主客观效果比传统方法和基于一般GA算法的方法好。 相似文献
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Multiobjective programming using uniform design and genetic algorithm 总被引:10,自引:0,他引:10
Yiu-Wing Leung Yuping Wang 《IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews》2000,30(3):293-304
The notion of Pareto-optimality is one of the major approaches to multiobjective programming. While it is desirable to find more Pareto-optimal solutions, it is also desirable to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to the decision maker. We design a genetic algorithm for this purpose. We compose multiple fitness functions to guide the search, where each fitness function is equal to a weighted sum of the normalized objective functions and we apply an experimental design method called uniform design to select the weights. As a result, the search directions guided by these fitness functions are scattered uniformly toward the Pareto frontier in the objective space. With multiple fitness functions, we design a selection scheme to maintain a good and diverse population. In addition, we apply the uniform design to generate a good initial population and design a new crossover operator for searching the Pareto-optimal solutions. The numerical results demonstrate that the proposed algorithm can find the Pareto-optimal solutions scattered uniformly over the Pareto frontier. 相似文献
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Speeding Up Fractal Image Compression by Genetic Algorithms 总被引:1,自引:0,他引:1
The main problem with all fractal compression implementation is execution time. Algorithms can spend hours to compress a single image. Most of the major variants of the standard algorithm for speeding up computation time have led to a bad-quality or a lower compression ratio. For example, the Fishers [7] proposed classification pattern greatly accelerated the algorithm, but image quality was poor due to the search-space reduction imposed by the classification, which eleminates a lot of good solutions.By using genetic algorithms to address the problem, we optimize the domain blocks search. We explore all domain blocks present in the image but not in exhaustive way (like a standard algorithm) and without omitting any possible block (solution) as a classification pattern does. A genetic algorithm is the unique method for satisfying these constraints. And it is a way to do be a random search because the genetic one is directed by fitness selection, which produces optimal solutions.Our goal in this work is to use a genetic algorithm to solve the IFS inverse problem and to build a fractal compression algorithm based on the genetic optimization of a domain blocks search. we have also implemented standard Barnsley algorithm, the Y. Fisher based on classification, and the genetic compression algorithm with quadtree partitioning. A population of transformations was evolved for each range block, and the result is compared with the standard Barnsely algorithm and the Fisher algorithm = based classification.We deduced an optimal set of values for the best parameters combination, and we can also specify the best combination for each desired criteria: best compression ratio, best image quality, or quick compression process. By running many test images, we experimentally found the following set of optimal values of all the algorithm parameters that ensure compromise between execution time and solutions optimality: Population size = 100, Maximum generations = 20, Crossover rate = 0.7, Mutation rate = 0.1, RMS limit = 5, Decomposition error limit = 10, Flips and isometrics count = 8.In our proposed algorithm, results were much better than those obtained both vences and Rudomin [5] and Lankhorst [4] approaches.First online version published in May 2005 相似文献
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针对单件小批量生产车间的优化排产问题,采用遗传算法进行研究,设计了一种分组编码方案,提出了可处理零部件间装配关系的适应度函数算法。介绍了遗传算法在车间优化排产中的实现方法及结果,重点讨论了编码方案及适应度函数设计。基于此算法,开发了生产作业计划排产软件,可完成顺排产与倒排产的自动排产,实现工艺排产的智能化。 相似文献
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基于NSCT和Tsallis熵的SAR图像快速分割方法 总被引:1,自引:0,他引:1
针对小波域SAR图像分割结果粗糙及运算速度低的不足,本文提出一种基于非下采样Contourlet变换(NSCT)和混沌粒子群优化(CPSO)的最大Tsallis熵的SAR图像快速分割方法。首先,利用NSCT提取SAR图像的概貌和细节信息,并建立相应的概貌-细节灰度级矩阵模型;然后,利用Tent映射CPSO算法搜索最优阈值,并提出递推算法大大减少迭代过程中适应度函数的重复计算。实验结果表明,与小波域SAR图像快速分割方法相比,该方法采用了具有多方向性和移不变性的NSCT分解图像,信息提取更为有效,分割结果更佳;同时由于引入混沌序列并以递推方式计算粒子适应度,粒子群搜索的收敛精度更高,运算时间更少。 相似文献
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