搜索资源列表
IPSO-based_ANN_with_SA
- 一种基于神经网络与模拟退火算法的改进粒子群优化算法，是英文-Based on neural network and simulated annealing algorithm to improve particle swarm optimization algorithm, is in English
LS-SVM_Based_on_PSO
- 基于混沌粒子群与模拟退火优化算法的最小二乘支持向量机参数自选择方法，是英文-Based on Chaotic Particle Swarm Optimization Algorithm and Simulated Annealing of least squares support vector machine parameters of self-selection method, is in English
PSO_B_SA
- 基于模拟退火的粒子群优化算法,示例程序，用于求解复杂函数的极值问题（源程序中的示例函数为Camel，Rastrigrin，Ackley）-PSO_A Alogrithm ,Hybrid particle swarm-based-simulated annealing optimization algorithm
psoandimprovedpso
- 基本粒子群优化算法和改进粒子群优化算法程序，包括：用基本粒子群算法求解无约束优化问题，用带压缩因子的粒子群算法求解无约束优化问题，用线性递减权重粒子群优化算法求解无约束优化问题，用自适应权重粒子群优化算法求解无约束优化问题，用随机权重粒子群优化算法求解无约束优化问题，用学习因子同步变化的粒子群优化算法求解无约束优化问题，用学习因子异步变化的粒子群优化算法求解无约束优化问题，用二阶粒子群优化算法求解无约束优化问题，用二阶振荡粒子群优化算法
PSO
- 基于模拟退火的粒子群算法，模拟退火算法在搜索过程中具有该概率突跳的能力，能够有效地避免搜索过程陷入局部极小解。-Based on simulated annealing particle swarm optimization, simulated annealing algorithm in the search process has a sudden jump in the probability of the capacity,
pso
- 粒子群优化算法是一种进化优化技术，源于对鸟群扑食的行为，是一种基于迭代的优化工具。此文件提供了基本粒子群算法、带压缩因子的粒子群算法、二阶粒子群算法、二阶振荡粒子群算法、权重改进的粒子群算法、混沌粒子群算法、基于杂交的粒子群算法、基于模拟退火的粒子群算法的MATLAB源代码。-PSO is an evolutionary optimization technique, derived from the behavior of the b
TSP-based-on-improved-pso
- 基于对粒子群优化算法原理的分析，实现了一种基于TSP的改进的粒子群优化算法：求解TSP的混合粒子群算法，结合遗传算法、蚁群算法和模拟退火算法的思想来解决TSP问题。-Particle swarm optimization based on the principle of the analysis, implemented based on TSP, improved particle swarm optimization algori
psooptimiton
- 粒子群求解无约束优化问题，包括基本粒子群算法，改进的粒子群算法，还有基于自然选择的粒子群算法和模拟退火的粒子群算法-Swarm for unconstrained optimization problems, including particle swarm algorithm, the improved particle swarm optimization, as well as natural selection based on
sa-pso-bybird
- 基于原始粒子群算法和原始模拟退火算法的结合。具有收敛性好，计算速度快的优势-Combined with the original particle swarm algorithm based on simulated annealing algorithm and the original. Has a good convergence, high calculation speed advantage
PSOPSA
- 基于模拟退火的粒子群算法，主要是将模拟退火得概率问题用于粒子群，使其不易陷入局部最优-Simulated annealing particle swarm algorithm based on simulated annealing was mainly probability for PSO, it is not easy to fall into local optima
PSO
- 各种粒子群或改进型粒子群算法 1）粒子群优化算法(求解无约束优化问题) 1>PSO(基本粒子群算法) 2>YSPSO(待压缩因子的粒子群算法) 3>LinWPSO(线性递减权重粒子群优化算法) 4>SAPSO(自适应权重粒子群优化算法) 5>RandWSPO(随机权重粒子群优化算法) 6>LnCPSO(同步变化的学习因子) 7>AsyLnCPSO(异步变化的学习因
peinen
- 基于K均值的PSO聚类算法,用于图像处理的独立分量分析，基于混沌的模拟退火算法。- K-means clustering algorithm based on the PSO, Independent component analysis for image processing, Chaos-based simulated annealing algorithm.
sai-tb67
- BP神经网络的整个训练过程,基于混沌的模拟退火算法，基于分段非线性权重值的Pso算法。- The entire training process BP neural network, Chaos-based simulated annealing algorithm, Based on piecewise nonlinear weight value Pso algorithm.
lui-kv21
- 仿真效率很高的，基于K均值的PSO聚类算法,基于混沌的模拟退火算法。- High simulation efficiency, K-means clustering algorithm based on the PSO, Chaos-based simulated annealing algorithm.
uh060
- 基于K均值的PSO聚类算法,能量熵的计算，基于混沌的模拟退火算法。- K-means clustering algorithm based on the PSO, Energy entropy calculation, Chaos-based simulated annealing algorithm.
PSO+2
- 基于模拟退火的PSO算法;用一个算列来说明该方法；同步变化学习因子PSO算法,也用一个算例来说明;本人运行测试通过(PSO algorithm based on simulated annealing; a column is used to illustrate the method; I run the test.)
模拟退火算法
- 提出了一种基于粒子群优化(PSO)算法的径向基(RBF)网络参数优化算法,首先利用减聚类算法确定网络径向基函数中心的个数,再用PSO算法优化径向基函数的中心及宽度,最后用PSO算法训练隐含层到输出层的网络权值,找到神经网络权值的最优解,以达到优化神经网络学习的目的。最后,通过一个实验与最小二乘法优化的神经网络进行了比较,验证了算法的有效性。(Particle swarm optimization (PSO) optimization o
13种PSO算法以及课件
- 各算法对应的问题如下： PSO 用基本粒子群算法求解无约束优化问题 YSPSO 用带压缩因子的粒子群算法求解无约束优化问题 LinWPSO 用线性递减权重粒子群优化算法求解无约束优化问题 SAPSO 用自适应权重粒子群优化算法求解无约束优化问题 RandWPSO 用随机权重粒子群优化算法求解无约束优化问题 LnCPSO 用学习因子同步变化的粒子群优化算法求解无约束优化问题 AsyLnCPSO
PSO
- 用二阶振荡粒子群优化算法、混沌粒子群优化算法、基于选择的粒子群优化算法、基于交叉遗传的粒子群优化算法、基于模拟退火的粒子群优化算法求解无约束优化问题(Second order oscillation PSO, chaotic particle swarm optimization algorithm, particle swarm optimization, genetic optimization algorithm based on
基于混沌自适应粒子群优化的matlab程序
- 文件包括带压缩因子的粒子群算法，权重改进的粒子群算法，自适应权重法，随机权重法，变学习因子的粒子群算法，异步变化的学习因子，二阶粒子群算法，二阶振荡粒子群算法，混沌粒子群算法，混合粒子群算法，杂交粒子群算法，模拟退火算法(Documents including particle swarm algorithm with compression factor, weighting the improved particle swarm a