搜索资源列表
ANNandSAA
- 用matlab编写的遗传算法和模拟退火程序-Matlab prepared using genetic algorithms and simulated annealing procedures
SAtlbx
- 模拟退火工具箱，程序主体和子函数含大量注解内容，非常适合学习者了解该算法的结构。 -Simulated Annealing toolbox, procedures and Functions with the main contents of a large number of comments, very suitable for learners to understand the structure of the algorithm.
six-humpcamelback
- 通用模拟退火优化算法 General simulated annealing algorithm 模拟退火优化算法能过较大限度的避免局部最优解 -General simulated annealing optimization algorithm General simulated annealing algorithm simulated annealing optimization algorithm can have a greater level of local optimal
devec3
- 微分进化算法DE是1995年由Rainer Storn和Kenneth Price首先提出。DE已被证明在求解过程中具有高效性、收敛性、鲁棒性等优点[5,6] 。它在许多优化问题中都表现出优于自适应模拟退火算法、POS 算法、GA算法的性能。DE利用实数值参数向量作为每一代的种群，它的自参考种群繁殖方案与其他优化算法不同。-DE was first proposed in 1995 by Rainer Storn and Kenneth Price and had been proven tha
geneticalgorithm
- 结合离散时间系统最优控制问题,提出一种新的混合算法.该算法是在遗传操作中嵌入模 拟退火算子,有效地结合了遗传算法隐含并行与模拟退火算法全局寻优的特点 -By embedding simulated annealing operator into genetic algorithm, a hybrid algorithm is put forward, which assimilates advantages ofboth genetic algorithm and simulated
wx1111
- 基于遗传和模拟退火算法的自动组卷系统设计与实现基于遗传和模拟退火算法的自动组卷系统设计与实现
opt4j-2.0
- 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
SAGA
- 用模拟退火优化遗传算法，使遗传算法具有反向搜索能力，通过仿真表明能够得到更优的值。-Optimization by simulated annealing genetic algorithm, genetic algorithm so that the reverse search capabilities, through the simulation shows that can be better value.
monituihuo
- 用模拟退火的算法解决中国旅行商的问题，结果画出图形直观明了！-Using simulated annealing algorithm to solve traveling salesman problem of China, resulting in intuitive and clear graphics to draw!
GODLIKE
- 遗传算法与模拟退火,粒子群算法的结合与比较实验.稍微修改就可以学习应用-Genetic algorithms and simulated annealing, particle swarm optimization algorithm and comparison with experiment. A slight modification can learn the application of
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求解无约束优化问题，用混沌粒子群优化算法求解无约束优化问题，
simann
- 用fortran语言写的模拟退火法的例子。模拟退火法作为一种全局优化算法，应用广泛，而fortran作为科学计算语言，是很多研究人员的首选，因此该程序容易被研究者集成到自己的程序中去。-Simulated annealing is a global optimization method, which is widely used, and fortran is a widely accepted scientific programing language, so this code is v
tabu_matlab
- 采用禁忌搜索算法和模拟退火法来求解旅行商问题，PDF文件，说明很详细-Using tabu search algorithm and simulated annealing method to solve the traveling salesman problem, PDF document describing in great detail
cpslssvm
- 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 and Simulated Annealing least squares support vector machine parameter self-selection method
tsp
- 利用模拟退火算法解决TSP问题（C++实现）-TSP PROBLEM
penumaral
- 通过分析均匀分布与Cauchy分布的分布机制，提出了一种改进的模拟退火图像盲复原算法，该算法选择Cauchy 分布为随机扰动量来产生状态扰动函数。通过计算机仿真，验证了该算法对初值的鲁棒性和复原的效果优于基于均匀分布随 机扰动量模拟退火盲解卷积算法，提高了收敛到最优解的速度 -After analyzing the distribution mechanisms of uniform distribution and Cauchy distribution,an algorith
PIDcontrowithAOC
- 除了蚁群算法，可用于PID参数优化的智能算法还有很多，比如遗传算法、模拟退火算法、粒子群算法、人工鱼群算法，等等。-In addition to ant colony algorithm can be used to optimize the PID parameters there are many intelligent algorithms, such as genetic algorithms, simulated annealing algorithm, particle swarm
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 birds of prey, is based on iter
Solving
- 求解双层规划问题常用的算法有极点算法、直接搜索法、下降法和非数值优化方法(如模拟退火算法、遗传算法等)，遗传算法的求解思路是：首先对上层的决策变量编码，代人下层规划模型，通过求解下层模型的决策变量值，代入上层模型计算适应度值，然后进行交叉、变异、选择操作，最后求出最优解。-Solving Bilevel Programming Problems with pole algorithm commonly used algorithms, direct search method, descent
SimuAPSO
- 模拟退火发与粒子群算法的结合，程序可用，收敛的很快-Simulated Annealing and Particle Swarm made the combination of procedures available, fast convergence