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monituihuo
- 模拟退火程序,一种运用广泛的智能算法，可求解优化问题！-Simulated annealing procedure, a use of a wide range of intelligent algorithm to solve the optimization problem!
BSSimuAPSOa
- 有基于模拟退火的粒子群算法优化化求解无约束优化问题可直接使用。 -Based on simulated annealing particle swarm algorithm optimization for unconstrained optimization problems can be used directly.
Metaheurisics
- 介绍遗传算法、模拟退火、一群算法、粒子群算法解决布局优化问题-Introduced genetic algorithm, simulated annealing, a group of algorithm, particle swarm optimization (pso) algorithm to solve layout optimization problems
facility-layout-problems
- 将布局问题转化为优化问题，利用只能优化算法求解，包括遗传算法、模拟退火、一群算法、粒子群算法-Will the layout problem is transformed into optimization problem, and use only optimization algorithm, including genetic algorithm, simulated annealing, a group of algorithm, particle swarm optimization
Flp_Asurvey_2007
- 介绍各种智能优化算法，包括遗传算法、模拟退火、一群算法、粒子群算法解决布局优化问题-Introduced all kinds of intelligent optimization algorithm, solve layout optimization problems
Niche-artificial-fish-swarm-alg
- 提出了一种基于生境人工鱼群算法的多峰问题优化算法．该算法融合了模拟退火、小生境技术的思想，并加入了变异算子和自动生成合适小生境半径机制-Since it is difficult to find all the optima when artificial fish SWalTn algorithm(AFSA)is used in multimodal optimization．a niche artificial fish swalTn algorithm(NAFSA)based on b
Particle-Swarm-Optimization
- 用基于交叉遗传的粒子群优化算法求解无约束优化问题；用基于模拟退火的粒子群优化算法求解无约束优化问题-Based on the cross with a genetic particle swarm optimization algorithm for solving unconstrained optimization problems using simulated annealing particle swarm optimization algorithm for solving unc
test2
- 模拟退火程序解TSP。C++编程用智能优化算法TSP问题，对初学者有一定帮助。-Simulated annealing process solution TSP. C++ programming with intelligent optimization algorithm TSP problem, some help for beginners.
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- 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-Based on Chaotic Particle Swarm Optimization Algorithm and Simulated Annealing least squares support vector machine parameters from selection method
3
- 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-Based on Chaotic Particle Swarm Optimization Algorithm and Simulated Annealing least squares support vector machine parameters from selection method
sa-tsp
- 应用模拟退火的TSP优化算法，程序注释清晰，运行流畅-The TSP simulated annealing optimization algorithm, the program notes clear and running smoothly
The-Multi-user-Detection-ALGORITHM
- 联合智能(JI-MUD)多用户检测算法是由粒子群优化(PSO)算法,遗传算法(GA)和模拟退火(SA)算法-the joint intelligent multi-user detection (JI-MUD) algorithm which was composed by particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and simulated annealing (SA) algorithm was p
PSOSA5
- 基于模拟退火的粒子群优化算法，算法的迭代速度快，精度高- particle swarm optimization with simulated annealing
PSO
- 粒子群优化算法容易陷入局部极值点、进化后期收敛速度慢、精度较差等缺点，提出混沌模拟退火粒子群优化（ＰＳＯ）算法-particle swarm optimization
testexample
- MATALB 优化算法，模拟退火解决TSP问题-MATALB optimization algorithm, simulated annealing to solve the TSP problem
optimization
- 基于Matlab的优化算法，文件里面包含模拟退火法等多种遗传算法。-Optimization algorithm based on Matlab, the file which contains a wide range of genetic simulated annealing algorithm
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
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
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- 基于模拟退火的粒子群优化算法，算法简单清晰，可得运行结果-Algorithm, the algorithm is simple and clear, available operating results on simulated annealing Particle Swarm
rengongzhinengjituxaingchuli
- 人工智能算法，模拟退火法，遗传算法，matlab数字图像处理，基于剩余矩形原理的矩形件排样优化程序-The layout optimization of rectangular parts has been studied, and the program is designed to solve the problem of rectangular layout optimization