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A new heuristic algorithm-ant colony optimization and its basical theory are introduced in this article to solve the complicated and optimized questions.This method simulates the course of ant searching food to solve this kind of questions.It has the
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linux平台下,在ns2里面实现的基于价格的分布式优化算法源代码-linux platform, in which the realization of ns2 Distributed price-based optimization algorithm source code
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经典的蚁群算法蚁周模型的实现，论文：《Distributed Optimization by Ant Colonies》-ant colony system, ant circle
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生成等角度均匀分布的网格点阵的c++程序，可以用于工程，数学等领域的按角度(alpha, belta, gamma)搜索优化问题。-Generate uniformly distributed angle of the grid lattice. The c++ program can be used for engineering, mathematics and other fields according to the angle (alpha, belta, gamma) searchi
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大师级人物写的蚁群算法解决TSP问题,真的挺不错,你可以下下来看看的.- Program s name: acotsp
Ant Colony Optimization algorithms (AS, ACS, EAS, RAS, MMAS, BWAS) for the
symmetric TSP
Copyright (C) 2004 Thomas Stuetzle
This program is free software yo
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PSOt, particle swarm optimization toolbox for matlab.
May be distributed freely as long as none of the files are
modified.
Send suggestions to bkbirge@yahoo.com
Updates will be posted pe
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多目标遗传算法工具箱，内容很详细，已经测试过，文件里面有怎么使用的说明-Micro Genetic Algorithm for Multiobjective Optimization，The code is distributed for academic purposes only. It has no warranty
implied or given, and the authors assume no liability for damage resulting from its u
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介绍了一种求解复杂优化问题的启发式算法--蚁群算法.该方法通过模拟蚁群对"信息素"的控制和利用进行搜索食物的过程-a distributed
algorithm for the solution of combinatorial optimization
problems which was inspired by the observation of real
colonies of ants. We apply ACS to both symmetric and
asymme
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Grid technologies have progressed towards a service-oriented paradigm that enables a new way
of service provisioning based on utility computing models, which are capable of supporting diverse
computing services. It facilitates scientific applicat
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：针对移动机器人传统路径规划算法效率不高，寻优能力差等问题，提出一种基
于神经网络和粒子群优化算法相结合的移动机器人路径规划方法．该方法利用神经网
络实现大量的并行和分布计算，发挥PSO简单、容易实现的优点，提高了路径规划的计
算效率和可靠性．仿真结果表明，这种新路径规划方法是可行且有效的．-The quality and eficiency of calculation is the two puzzling problems in the tradi—
tional algo
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蚁群算法是一种用来在图中寻找优化路径的机率型算法。是一种求解组合最优化问题的新型通用启发式方法，该方法具有正反馈、分布式计算和富于建设性的贪婪启发式搜索的特点。通过建立适当的数学模型，基于故障过电流的配电网故障定位变为一种非线性全局寻优问题。-Ant colony algorithm is a method used to find optimal path in the graph the probability-based algorithm. Is a combination of opt
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基于 MAS 建立的分布并行计算环境，其任务调度问题具有新的特点 对于基于MAS 的配电快速仿真与模拟系统，以配电
网三相状态估计为例，建立了新的调度问题模型，提出了基于FCM 的启发式任务调度优化方法 该方法利用模糊C 均值聚类分析
算法对独立任务进行聚类分析，避免了在超大解空间中用基于搜索的方法去寻求最优解的做法 该方法具有线性时间复杂度，而
且通过大量算例表明它总是能够在合理的时间内获得次优解或最优解，使masDSE 的性能有明显改善-In the distributed pa
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A distributed PSOSVM hybrid system with feature selection and parameter optimization
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This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to
improve the clas
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1 分布式数据库系统概述
2 分布式数据库系统的设计
3 分布式数据库中的查询处理和优化
4 分布式数据库中的事务管理和恢复
5 分布式数据库中的并发控制
6 分布式数据库中的可靠性
7 分布式数据库的安全性与目录管理
8 分布式数据库与客户机/服务器模式
9 分布式数据库与WWW数据库和移动数据库
10 分布式数据库系统的发展趋势
11 数据仓库和数据挖掘-An overview of distributed database systems distr
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基于通信量优化的分布式组件部署策略的研究Optimization based on traffic studies distributed component deployment strategy-Optimization based on traffic studies distributed component deployment strategy
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o Client-server computing or networking is a distributed application architecture that partitions tasks or workloads between service providers (servers) and service requesters, called clients. Often clients and servers operate over a computer network
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Optimization queries form an important part of real world DBSs. Researches in global query optimization have been scarce compared to other issues (such as schema integration) due to the unavailability to reliable cost estimate for autonomous DBCs. Th
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最早提出细菌觅食算法的经典之作。biomimicry of bacterial foraging for distributed optimization and control-the firstly proposed paper about bacterial foraging optimization named biomimicry of bacterial foraging for distributed optimization and control。
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使用FFR对各宏小区的用户进行资源分配，在多用户随机分布着各个小区中的情况下，统计中心小区和边缘小区的用户吞吐量。-The resource allocation is performed for the users of each macro cell using the FFR, and the user throughput of the center cell and the edge cell is counted when the multi-user is randomly dis
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Quantum-behaved particle swarm optimization with Gaussian
distributed local attractor point
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