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2下载：
基于matlab的基本粒子群优化算法，可应用与最优潮流计算等-Matlab-based optimization of the particle swarm algorithm can be applied with optimal power flow calculation
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微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年开发的一种演化计算技术, 来源于对鸟群捕食过程的模拟。PSO同遗传算法类似，是一种基于叠代的优化工具，但与遗传算法使用遗传操作子进行优化不同，利用群体中各个体之间的“协作”与“竞争”关系，根据自身及其竞争者的飞行经验，调整自己的行为。同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域。,Particle Swarm Opt
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------名称：带交叉因子的改进PSO算法
------功能：求解多维无约束优化问题
------特点：收敛性强，还可以加入变异算子,------ Name: with cross-factor function to improve the PSO algorithm ------: Solving multi-dimensional unconstrained optimization problem ------ characteristics: strong converge
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基于差分进化的粒子群优化算法，（全局最优粒子群优化算法）-Based on differential evolution particle swarm optimization, (global optimization PSO)
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％--- 名称：带交叉因子的改进PSO算法
％------功能：求解多维无约束优化问题
％------特点：收敛性强，还可以加入变异算子
---- Name: with cross-factor function to improve the PSO algorithm------: Solving multi-dimensional unconstrained optimization problems------ characteristics: strong convergen
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Multiobjective Particle Swarm with Crowding Distance（多目标优化）-Multiobjective Particle Swarm with Crowding Distance (multi-objective optimization)
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将粒子群算法与免疫结合，计算函数的优化极值。-The particle swarm optimization combined with the immune calculated extremal function optimization.
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带交叉因子的粒子群MATLAB算法程序，可实现带约束变量的微粒群算法-With cross-factor PSO algorithm MATLAB procedures, can be constrained variable particle swarm optimization
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带交叉因子的改进PSO算功能：求解多维无约束优化问题收敛性强，还可以加入变异算子-With cross-factor improvement of PSO operator functions: Solving multi-dimensional unconstrained optimization problem of strong convergence can also add mutation operator
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时变参数的粒子群优化的matlab实现，可用于解决多种连续优化问题-A flexible implementation of PSO algorithm with time-varying parameters. Algorithm is suitable for solving continuous optimization problems. Special care has been taken to enable flexibility of the algorthm with resp
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Java平台的启发式优化算法，包含了多目标进化算法（SPEA2和NSGA2），多目标差异进化，PSO和单目标模拟退火算法。并且包含了ZDT,DTLZ和WFG等测试函数-Opt4J is a framework for applying meta-heuristic optimization algorithms to arbitrary optimization problems.
The Opt4J framework currently includes a multi-obje
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example of matlab,optimization method which is used with different model.it will be useful for whom they want to learn Matlab by themselves1
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pso粒子群算法与隐马尔科夫模型的结合，用粒子群优化算法度模型参数进行优化，用于度序列观察。-pso particle swarm algorithm with a combination of hidden Markov model, using particle swarm optimization algorithm to optimize the degree of model parameters for the degree of sequence observations.
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用于单序列观察。pso粒子群算法与隐马尔科夫模型的结合，用粒子群优化算法度模型参数进行优化，这个适用于单序列观察-For single sequence of observations. pso particle swarm algorithm with a combination of hidden Markov model, using particle swarm optimization algorithm to optimize the degree of model paramete
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自适应权重的粒子群算法，实现复杂问题的有效求解(Particle Swarm Optimization with Adaptive Weight for Effective Solution of Complex Problems)
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采用粒子群算法求解多目标优化问题，有注释(Using particle swarm optimization to solve multi-objective optimization problems with annotations)
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未来高比例可再生能源电力系统愿景下，具有波动
性和不确定性的风/光等可再生能源作为主要能源供应者，给
电力系统带来了巨大的灵活性需求，考虑单一类型灵活性资
源提供调节能力的规划模型和以净负荷包络线为基础的电源
规划方法将不再适用。引入灵活性指标量化评估源荷储多类
型灵活性资源的调节潜力并作为优化决策变量纳入到规划模
型中(Under the vision of the high penetration renewable energy power systems in the futu
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粒子群算法求解标准VRP问题，带有十个小型算例，可运行，适合新手学习。(Particle swarm optimization (PSO) is used to solve the standard VRP Problem, with ten small examples, which can be run, and is suitable for novice learning.)
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该算法中将速度位移公式与遗传算法相结合用于结果解决多配送中心的路径优化问题(In this algorithm, the velocity displacement formula is combined with the genetic algorithm to solve the path optimization problem of multiple distribution centers)
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利用主成分分析法结合粒子群（PSO）优化极限学习机(ELM)进行工程费用估计预测(In this paper, principal component analysis (PCA) combined with particle swarm optimization (PSO) optimization extreme learning machine (ELM) is used to estimate and forecast engineering cost)
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