搜索资源列表
IPSO-FOR-FUNCTION
- 一种改进的粒子群优化算法求解约束优化复杂问题-An improved particle swarm optimization algorithm for solving constrained optimization of complex problems!!
Quantum--(pso)-algorithm
- 针对单级多资源约束生产批量计划问题,提出了基于量子粒子群算法求解该问题的方法。此算法将量子强大的领域搜索能力和基本粒子群算法(PSO)通过跟踪极值更新粒子的功能-On a single stage production lot-sizing problem of resource constraints, based on quantum particle swarm algorithm is proposed to solve the problem.The algorithm to the
Optimization-design-test
- 从数控机床能耗角度出发，以切削参数为变量，以降低数控机床能耗为目标，在实际加工经验公式的基础上，考虑机床性能和刀具约束条件，建立数控机床能耗模型，采用粒子群优化算法对目标函数寻优求解，利用优化后的切削参数进行加工，能明显地降低能耗。-Optimization design of numerical control tool energy base on cutting paramenters
Optimization-design-experiment1
- 从数控机床能耗角度出发，以切削参数为变量，以降低数控机床能耗为目标，在实际加工经验公式的基础上，考虑机床性能和刀具约束条件，建立数控机床能耗模型，采用粒子群优化算法对目标函数寻优求解，利用优化后的切削参数进行加工，能明显地降低能耗。-From the perspective of CNC machine tool consumption to cutting parameters as variables , in order to reduce the energy consumption o
Optimization-design-experiment2
- 从数控机床能耗角度出发，以切削参数为变量，以降低数控机床能耗为目标，在实际加工经验公式的基础上，考虑机床性能和刀具约束条件，建立数控机床能耗模型，采用粒子群优化算法对目标函数寻优求解，利用优化后的切削参数进行加工，能明显地降低能耗。-From the perspective of CNC machine tool consumption to cutting parameters as variables , in order to reduce the energy consumption o
particle-swarm-multiple
- 粒子群多目标优化用于工业工程，具有约束条件下的多目标优化设计。- objective optimization of particle swarm multiple
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(异步变化的学习因子)(算法还有bug) 8>SecPSO(用二阶粒
pso1
- 基于粒子群算法的电力系统负荷分配问题（1）数学模型： 以IEEE 3机6节点为工况模型 ，Load=850MW （2）目标函数： minF=∑(i=1，Ng)Fi(PGi)，Fi(PGi) =aiPi2+biPi+ci+Ei，考虑阀点(Valve-Point)效应 （3）约束条件： a.发电机组输出功率上下限约束，即PGi min<=PGi<= PGimax； b.电力负荷平衡约束，忽略网损，即∑(i=1，Ng)（PGi）= PGD-Based on part
imccmolibing
- 使用粒子群免疫优化算法解决含多重约束条件的二维CMOP1问题。-A particle swarm immune optimization algorithm is used to solve the problem of two-dimensional CMOP1 with multiple constraints.
PSO-noncon
- 粒子群算法，可以计算含非线性不等式约束和等式约束的优化问题。-PSO algorithm can be calculated with nonlinear inequality constrained optimization problems and equality constraints.
ycsf
- matlab 遗传算法GA，粒子群算法PSO,蚁群算法AS 前段时间上智能计算方法实验课上，自己做的程序。帖到这里，希望有人能改进它们，交流经验这样更有价值。 遗传算法解决最小生成树问题，PURFER编码。 粒子群算法做无约束最优化问题。 蚁群算法解决TSP问题。 -matlab genetic algorithm GA, particle swarm optimization PSO, some time ago on the ant colony algorithm intelligent
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 cross particle swarm optimiza
01
- 二维、三维约束性粒子群算法。可直接使用，若要使用对自己的函数则将目标函数进行修改即可- Two-dimensional, three-dimensional binding particle swarm optimization. Can be used directly, to use for their function will be objective function can be modified
AS_GA_PSO
- 遗传算法解决最小生成树问题，PURFER编码。 粒子群算法做无约束最优化问题。 蚁群算法解决TSP问题。-Genetic algorithm (ga) to solve minimum spanning tree problem, PURFER encoding.Particle swarm algorithm for unconstrained optimization problems.Ant colony algorithm to solve TSP problem.
PSO-GA
- 粒子群算法求解多维约束函数极值，并与遗传算法比较。结果发现，粒子群有很好的精度。-A particle swarm optimization algorithm for solving the extreme value of multi dimensional constrained function, and compared with genetic algorithm. The results show that the particle swarm has a good accurac
discrete-mopso
- 离散多目标粒子群优化算法，解决含约束条件的多目标离散问题-discrete mopso
YSPSO
- 一个很有用的带压缩因子粒子群算法的matlab程序，用于求解多维无约束优化问题。-A useful matlab code of particle swarm algorithm with compression factors, it can be used to solve the multidimensional unconstrained optimization problems.
AsyLnCPSO
- 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-Using asynchronous learning factor variation of particle swarm optimization algorithm for solving unconstrained optimization problems, are helpful to you!
BreedPSO
- 用基于交叉遗传的粒子群优化算法求解无约束优化问题-Based on cross genetic particle swarm optimization algorithm for solving unconstrained optimization problems, are helpful to you!
CLSPSO
- 用混沌粒子群优化算法求解无约束优化问题,希望对大家有帮助！-Chaotic particle swarm optimization algorithm for solving unconstrained optimization problems, I hope it can help you!