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5下载：
细菌觅食随机优化论文及matlab源码。该算法属于进化算法的一种，可以处理全局优化、多目标优化、约束优化和动态优化等问题。,Bacterial feeding stochastic optimization papers and matlab source code. The algorithm belongs to a kind of evolutionary algorithm that can deal with global optimization, multi-objective o
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This thesis aims to clearly describe the cognitive radio and its components and operations. Moreover, it aims on describing the expected outcome from the most common techniques that are proposed for use in cognitive radios. In addition, it describes
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它包含了枚举法、蒙特卡洛法、线性整数规划、整数规划枚举法、整数规划隐枚举法、非线性整数规划、非线性整数规划图形工具、最小生成树kruskal算法、最短路dijkstra算法、最小生成树kruskal算法mex程序、最短路dijkstra算法mex程序和动态规划的代码。-It contains the matlab code of the enumeration method, Monte Carlo method, linear integer programming, integer prog
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动态贝叶斯网络结构学习算法，用来检验基于BOA的DBN结构寻优体系的合理性与可行性。环境matlab 6.1以上-Dynamic Bayesian network structure learning algorithm, the DBN used to test the structure-based optimization BOA system is reasonable and feasible. Environmental matlab 6.1 or above
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ve-DYNA® 为用户提供了车辆动力学、车辆非线性行为的可配置仿真模型。用户根据自己的工程问题选择合适的车型（轿车，货车，拖车）和适当的版本（低级，标准，高级）就能实现不同的应用。用户基于模型就能开发自己的控制算法或者部件，然后通过离线仿真和硬件在回路仿真来进行检验和验证。只需要进行鼠标键盘的操作，就可以对种种的动力学问题进行分析，比如悬架动力学，车辆动力性或操纵稳定性。这样 就 能够减少昂贵而且费时甚至是危险的实车试验。可以在无人监控的情况下完成整个的测试、优化和系统验证 。本文为v
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细菌优化算法程序，通过动态的演示说明细菌优化算法的优化过程，从而反应了该算法的有效性。-Bacteria optimization process, through dynamic optimization algorithm demonstrates that the optimization process of bacteria, which reflects the effectiveness of the algorithm.
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多目标优化进化算法目前公认效果收敛性最好的算法NSGA2c++源码,具有一般性，可在此基础上继续改进，对实现其他多目标优化算法很有帮助.-Multi-objective optimization evolutionary algorithm is currently the best recognized effect of convergence of the algorithm NSGA2c++ Source, with the general, could be on this basis
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平衡二叉树是数据结构中一个非常重要的概念。它对二叉树的优化和提高查询效率有重要的作用，它是动态查找的一个非常重要方法，它在实际生产中有着广泛的应用。
通过本课程设计教学所要求达到的目的是：充分理解和掌握二叉树、平衡二叉树的相关概念和知识。掌握平衡二叉树的生成、结点删除、插入等操作过程，并编程实现从键盘上输入一系列数据（整型），建立一棵平衡二叉树，任意插入或删除一个结点后仍然要求构成平衡二叉树，并按中序遍历输出这棵平衡二叉树。
-Balanced binary tree data s
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These are matlab and simulink files to model the membrane crystallization system, including the matlab file to get the optimation point of this system, and 3 simulink files, which are static model and 2 dynamic models. There has PID control and feed
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A New Dynamic Multi-objective Optimization Evolutionary Algorithm
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使用动态规划求多段图的C++源程序，用于满足最优化问题且问题能转换成一个有向图问题的求解，可以直接运行！-The use of dynamic programming for multi-stage plan of C++ source code, designed to meet the optimization problem and the problem can be converted into a directed graph to solve the problem, you ca
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Multiobjective optimization for dynamic environments.
The IEEE Congress on Evolutionary Computation (CEC)
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最新SD期刊上关于改进型PSO算法，我是通过学校内部数据库在SD下载的哦！包括《A dynamic inertia weight particle swarm optimization algorithm》、《Adaptive Particle Swarm Optimization》、《Cyber Swarm Algorithms – Improving particle swarm optimization using adaptive memory strategies》。这三篇都是比较有研
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Matlab车间动态调度PSO微粒群算法程序（优化目标为机器间隙时间）-Matlab Workshop Particle Swarm Optimization PSO dynamic scheduling procedures (optimization target machine clearance time)
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Matlab 车间动态调度 PSO微粒群算法程序（优化目标为平均流动时间）-Matlab Workshop Particle Swarm Optimization PSO dynamic scheduling program (average flow time optimization objectives)
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Genetic algorithms (GAs) have recently been accepted as powerful approaches to
solving optimization problems. It is also well-accepted that building block construction
(schemata formation and conservation) has a positive influence on GA behavior.
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用基本遗传算法求解一维无约束优化问题
用顺序选择遗传算法求解一维无约束优化问题
用动态线性标定适应值的遗传算法求解一维无约束优化问题
用大变异遗传算法求解一维无约束优化问题
用自适应遗传算法求解一维无约束优化问题
用双切点遗传优化求解一维无约束优化问题
用多变异位自适应遗传优化求解一维无约束优化问题
-The basic genetic algorithm with one-dimensional sequence of unconstrained optimizat
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采用改进动态寻优方法，并结合单点、改进的交叉算子与轮盘赌、两两竞争遗传算子分别求解函数最小值，仿真结果表明其差异与优劣。-Improved dynamic optimization method, combined with a single point, improved crossover and roulette, pairwise genetic operators were competing to solve the minimum function, simulation resu
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cec2009动态优化问题的函数测试集。(The benchmarks for CEC2009 Special Session / Competition on Dynamic Optimization)
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动态算法的讲解，经典例题和相应的源代码，是学习动态规划的好材料(dynamic programming (also known as dynamic optimization) is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing
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