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1.
基于渗透原理迁移策略的并行遗传算法   总被引:9,自引:0,他引:9  
赖鑫生  张明义 《计算机学报》2005,28(7):1146-1152
通过分析影响并行遗传算法性能的诸多因素,以避免人为设置迁移代频、迁移率及迁移方向为问题的突破口,以减少通信量提高算法效率为主旨,提出一种基于渗透原理的迁移策略(Migration Scheme Based On Penetration,PMS).PMS迁移策略源于渗透模型,引入渗透阈值控制相邻子群体的迁移,应用渗透原理自适应地确定迁移代频、迁移率及迁移方向,从而解决人为设置迁移代频、迁移率及迁移方向的关键问题,有效降低通信代价,进而提高算法效率.文中首先依据有限群体马尔可夫链模型对基于渗透原理的迁移策略算法的可行性进行了探讨,然后从理论角度给出了迁移代频期望、迁移率期望及通信代价,同时用实例验证了PMS在降低通信代价方面的巨大潜力.  相似文献   

2.
通过分析模式定理及建筑块理论,提出一种基于建筑块迁移策略并行遗传算法.算法根据种群的收敛情况,从其他种群中获取非重叠的建筑块,采用模拟退火思想防止优良模式的浓度过快地增大引起早熟.理论分析和对多峰函数的仿真结果均表明,该算法减少了无效迁移次数,降低了通信开销,而且发生成熟前收敛的概率明显下降,保证了遗传算法的全局收敛性.  相似文献   

3.
迁移策略是移动Agent(Mobile Agent,MA)的核心技术之一,MA的效率很大程度上取决于迁移策略的优化。本文提出了一种改进的分布式遗传算法(EDGA),用于对多约束条件下MA迁移策略最优问题进行求解。EDGA将分布式遗传算法和Cascade模型相结合,在迁移算子部分设计一个中心监控器,观察每个子种群的进化,并对迁移个体的选择以及相应子种群的大小做出调整,使进化能力好的子种群得到更大的空间来搜索最优值。实验结果表明:本文所提出的EDGA算法在求解速度和质量上取得了较大的改善。  相似文献   

4.
提出一种适应性分布式差分进化算法.将初始种群分为多个子种群,并设计子种群间的迁移机制,当满足迁移条件时,根据冯?诺依曼拓扑结构,子种群内的优秀个体代替其邻域的较差个体,使得整个种群实现信息共享.同时,根据个体适应值变化情况,对每一个体分配不同的缩放因子?和交叉率CR,提出?和CR的适应性策略.实验结果表明,所提出算法有利于对解空间进行广泛探索,避免算法陷入早熟收敛,能够搜索到性能较好的解.  相似文献   

5.
在已有的多种群粒子群文化算法知识迁移策略中,迁移知识不一定能反映优势区域中的较优点.为提高知识迁移效率,在知识迁移机制中引入混沌搜索策略,提出一种多种群粒子群文化算法的混沌知识迁移策略.它利用混沌序列对迁移单元进行深入探索,以提高迁移知识的有效性;根据进化代数动态调整知识迁移间隔,从而在进化前期维持种群的多样性,在进化后期加速种群收敛.数值计算结果表明,该算法可以有效提高进化收敛速度,帮助子种群跳出局部较优解.  相似文献   

6.
竞争合作型协同进化免疫算法及其在旅行商问题中的应用   总被引:2,自引:0,他引:2  
为提高人工免疫算法的收敛性能,提出了一种竞争合作型协同进化免疫优势克隆选择算法(CCCICA).把生态学中的协同进化思想引入到人工免疫算法中,考虑了环境和子群间相互竞争的关系,子种群内部通过局部最优免疫优势,克隆扩增,自适应动态高频混合变异等相关算子的操作加快了种群亲和度成熟速度.把信息熵理论引入到算法中完善了种群的多样性.所有子种群共享同一高层优良库,并将其作为抗体子种群领导集合,对高层优良种群进行免疫杂交操作,通过迁移操作把优良个体返回到各子种群,实现了整个种群信息交流与协作.针对旅行商问题(traveling salesman problem,TSP)多个实例结果表明:与其它智能算法相比较该算法具有较好的性能.  相似文献   

7.
提出一种动态环境下基于预测机制的多种群进化算法,将预测机制引入到动态进化算法的研究中,对算法所得的某些信息进行记忆,根据记忆序列构建预测模型,当环境发生变化时能够通过预测模型对动态环境进行预先判断.算法采用自组织侦查的多种群策略,多个子种群对搜索子空间进行局部搜索,主种群用于确定新的搜索子空间.在子种群的自适应调整、子种群间的拥挤操作等方面进行了改进,根据子种群所跟踪的最优解位置信息构建预测模型,当环境发生变化时通过预测及子种群的进化实现对动态环境的自适应跟踪.以移动峰问题为测试对象,实验结果表明新算法具有良好的处理动态问题的能力.  相似文献   

8.
现有进化算法大都从问题的零初始信息开始搜索最优解, 没有利用先前解决相似问题时获得的历史信息, 在一定程度上浪费了计算资源.将迁移学习的思想扩展到进化优化领域, 本文研究一种基于相似历史信息迁移学习的进化优化框架.从已解决问题的模型库中找到与新问题匹配的历史问题, 将历史问题对应的知识迁移到新问题的求解过程中, 以提高种群的搜索效率.首先, 定义一种基于多分布估计的最大均值差异指标, 用来评价新问题与历史模型之间的匹配程度; 接着, 将相匹配的历史问题的知识迁移到新问题中, 给出一种基于模型匹配程度的进化种群初始化策略, 以加快算法的搜索速度; 然后, 给出一种基于迭代聚类的代表个体保存策略, 保留求解过程中产生的优势信息, 用于更新历史模型库; 最后, 将自适应骨干粒子群优化算法嵌入到所提框架, 给出一种基于相似历史信息迁移学习的骨干粒子群优化算法.针对多个改进的典型测试函数, 实验结果表明, 所提迁移策略可以加速粒子群的搜索过程, 显著提高算法的收敛速度和搜索效率.  相似文献   

9.
结合动态概率粒子群优化算法(DPPSO)特点,针对传统的单种群粒子群优化算法易陷入局部最优、收敛速度较慢的缺点,文中提出一种基于异构多种群策略的DPPSO.该算法在进化过程中保持多个子种群,每个子种群以不同的DPPSO变体进行进化,子种群之间根据一定规律进行通信,从而保持整个种群内部的信息交流,进而协调DPPSO的勘探和开采能力.通过典型的Benchmark函数优化问题测试并分析基于异构多种群策略的DPPSO性能,结果显示,使用该策略的算法收敛速度较快,稳定性有较显著提高,具有较强的全局搜索能力.  相似文献   

10.
针对现有自组织迁移算法(SOMA)只能求解单个优化问题及其“隐并行性”未能被充分挖掘的缺陷,提出信息筛选多任务优化自组织迁移算法(SOMAMIF)实现同一时刻处理多个优化问题。首先,构造多任务统一搜索空间,并根据任务个数设置相应的子种群;然后,对各子种群当前最优适应值进行判断,当任务连续若干代停滞进化时则产生信息交互需求;接着,按概率从剩余子种群中筛选对自己有用的信息并过滤无用信息,从而在保证信息正向迁移同时实现种群结构的重新调整;最后对算法的时间复杂度和空间复杂度进行分析。实验结果表明,SOMAMIF在同时求解多个高维函数优化问题时均快速收敛至全局最优解0,而SOMAMIF与分形技术相结合同时提取不同户籍高校学生返乡关键制约因素时,其在两个数据集上得到的平均分类准确率与原始数据集的平均分类准确率相比分别提高了0.348 66个百分点和0.598 57个百分点。  相似文献   

11.
Schema theory is the most well-known model of evolutionary algorithms. Imitating from genetic algorithms (GA), nearly all schemata defined for genetic programming (GP) refer to a set of points in the search space that share some syntactic characteristics. In GP, syntactically similar individuals do not necessarily have similar semantics. The instances of a syntactic schema do not behave similarly, hence the corresponding schema theory becomes unreliable. Therefore, these theories have been rarely used to improve the performance of GP. The main objective of this study is to propose a schema theory which could be a more realistic model for GP and could be potentially employed for improving GP in practice. To achieve this aim, the concept of semantic schema is introduced. This schema partitions the search space according to semantics of trees, regardless of their syntactic variety. We interpret the semantics of a tree in terms of the mutual information between its output and the target. The semantic schema is characterized by a set of semantic building blocks and their joint probability distribution. After introducing the semantic building blocks, an algorithm for finding them in a given population is presented. An extraction method that looks for the most significant schema of the population is provided. Moreover, an exact microscopic schema theorem is suggested that predicts the expected number of schema samples in the next generation. Experimental results demonstrate the capability of the proposed schema definition in representing the semantics of the schema instances. It is also revealed that the semantic schema theorem estimation is more realistic than previously defined schemata.  相似文献   

12.
Semantic schema theory is a theoretical model used to describe the behavior of evolutionary algorithms. It partitions the search space to schemata, defined in semantic level, and studies their distribution during the evolution. Semantic schema theory has definite advantages over popular syntactic schema theories, for which the reliability and usefulness are criticized. Integrating semantic awareness in genetic programming (GP) in recent years sheds new light also on schema theory investigations. This paper extends the recent work in semantic schema theory of GP by utilizing information based clustering. To this end, we first define the notion of semantics for a tree based on the mutual information between its output vector and the target and introduce semantic building blocks to facilitate the modeling of semantic schema. Then, we propose information based clustering to cluster the building blocks. Trees are then represented in terms of the active occurrence of building block clusters and schema instances are characterized by an instantiation function over this representation. Finally, the expected number of schema samples is predicted by the suggested theory. In order to evaluate the suggested schema, several experiments were conducted and the generalization, diversity preserving capability and efficiency of the schema were investigated. The results are encouraging and remarkably promising compared with the existing semantic schema.  相似文献   

13.
We review the main results obtained in the theory of schemata in genetic programming (GP), emphasizing their strengths and weaknesses. Then we propose a new, simpler definition of the concept of schema for GP, which is closer to the original concept of schema in genetic algorithms (GAs). Along with a new form of crossover, one-point crossover, and point mutation, this concept of schema has been used to derive an improved schema theorem for GP that describes the propagation of schemata from one generation to the next. We discuss this result and show that our schema theorem is the natural counterpart for GP of the schema theorem for GAs, to which it asymptotically converges.  相似文献   

14.
一种基于模式分析的防止遗传算法过早收敛的方法   总被引:1,自引:0,他引:1  
张羽飞  冯汝鹏 《信息与控制》2004,33(1):23-26,30
本文提出一种遗传算法中模式的表示方法和个体间最大共有模式的获取方法,并以此为基础提出了基于模式分析的种群插入策略来解决遗传算法过早收敛问题.通过与其他种群插入算法的对比证明该 方法的有效性.给出了采用该种群插入策略的遗传算法的收敛性定理及其证明.  相似文献   

15.
粗糙集理论在图像增强中的应用   总被引:26,自引:0,他引:26  
粗糙集理论是一种新的处理含糊和不确定性问题的数学工具。它作为一种软计算方法,与模糊方法、遗传算法、神经网络等一样,是有发展潜力的智能信息处理方法。本文提出一种基于粗糙集的图像增强新方法,该方法按条件属性,将一幅图像划分为不同的子图,然后对子图分别作对比度增强,增强图像的效果较为理想,满足了工程上的要求。  相似文献   

16.
遗传算法的收敛性研究   总被引:27,自引:1,他引:27  
王丽薇  洪勇 《计算机学报》1996,19(10):794-797
本文讨论了遗传算法的收敛性问题,提出了一个收敛的充分条件,证明了对任何问题,只要其问题空间编码和遗传操作的组合满足这个条件,就可以用遗传算法求解,由此得到了GGA-难题珠新定义,解释了现有模式理论所不能解释的最小欺骗问题,并讨论了它的可操作性。  相似文献   

17.
Nowadays, the SMS is a very popular communication channel for numerous value added services (VAS), business and commercial applications. Hence, the security of SMS is the most important aspect in such applications. Recently, the researchers have proposed approaches to provide end-to-end security for SMS during its transmission over the network. Thus, in this direction, many SMS-based frameworks and protocols like Marko's SMS framework, Songyang's SMS framework, Alfredo's SMS framework, SSMS protocol, and, Marko and Konstantin's protocol have been proposed but these frameworks/protocols do not justify themselves in terms of security analysis, communication and computation overheads, prevention from various threats and attacks, and the bandwidth utilization of these protocols. The two protocols SMSSec and PK-SIM have also been proposed to provide end-to-end security and seem to be little better in terms of security analysis as compared to the protocols/framework mentioned above. In this paper, we propose a new secure and optimal protocol called SecureSMS, which generates less communication and computation overheads. We also discuss the possible threats and attacks in the paper and provide the justified prevention against them. The proposed protocol is also better than the above two protocols in terms of the bandwidth utilization. On an average the SecureSMS protocol reduces 71% and 59% of the total bandwidth used in the authentication process as compared to the SMSSec and PK-SIM protocols respectively. Apart from this, the paper also proposes a scheme to store and implement the cryptographic algorithms onto the SIM card. The proposed scheme provides end-to-end SMS security with authentication (by the SecureSMS protocol), confidentiality (by encryption AES/Blowfish; preferred AES-CTR), integrity (SHA1/MD5; preferred SHA1) and non-repudiation (ECDSA/DSA; preferred ECDSA).  相似文献   

18.
基于模式分析的遗传算法种群插入策略   总被引:1,自引:0,他引:1  
提出了一种遗传算法中模式的表示方法和个体间最大共有模式的获取方法,并以此为基础提出了基于模式分析的种群插入策略来解决遗传算法过早收敛问题,通过与其他种群插入算法的对比证明该方法的有效性,并给出采用该种群插入策略的遗传算法的收敛性定理及其证明。  相似文献   

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