搜索资源列表
遗传模拟退火算法
- 介绍了遗传算法和退火算法的结合使用,提高了GA本身的性能(introduce a method combine GA with SA to make a difference to GA)
模拟退火禁忌搜索遗传算法神经网络MATLAB程序合集
- 模拟退火,禁忌搜索,遗传算法,神经网络-MATLAB程序合集(Simulated annealing, tabu search, genetic algorithms, neural networks - MATLAB collection)
MATLAB智能算法30个案例分析
- 对于各种现今时髦的算法的分析,里面有三十种算法,包含蚂蚁算法,遗传算法,模拟退火等等。(For the analysis of modern algorithms, there are thirty algorithms, including ant algorithm, genetic algorithm, simulated annealing, and so on)
智能算法
- 智能算法,含有遗传算法、模拟退火算法、BP神经网络优化、免疫算法、粒子群算法、蚁群算法等智能算法,MATLAB亲测可用。(Intelligent algorithm, including genetic algorithm, simulated annealing algorithm, BP neural network optimization, immune algorithm, particle swarm algorithm, ant colony algorithm and other
chapter5
- 基于模拟退火算法解决TSP问题,包括源代码和一些主要函数(Based on simulated annealing algorithm to solve the TSP problem, including source code and some major functions)
Auto_Path
- 利用MATLAB语言模拟退火算法和遗传算法这两个算法结合构成的遗传模拟退火算法对移动机器人进行路径规划(Using MATLAB simulated annealing algorithm and genetic algorithm two genetic algorithms combined with genetic simulated annealing algorithm for mobile robot path planning)
智能优化算法资料
- 优化算法有很多,经典算法包括:有线性规划,动态规划等;改进型局部搜索算法包括爬山法,最速下降法等,模拟退火、遗传算法以及禁忌搜索称作指导性搜索法。而神经网络,混沌搜索则属于系统动态演化方法。 梯度为基础的传统优化算法具有较高的计算效率、较强的可靠性、比较成熟等优点,是一类最重要的、应用最广泛的优化算法。但是,传统的最优化方法在应用于复杂、困难的优化问题时有较大的局限性。(There are many optimization algorithms, the classical algori
模拟退火,遗传算法,神经网络程序
- 模拟退火,遗传算法,神经网络程序高级算法的简单运用,是有效的计算出最优的方法,相比于暴力搜索,算法简洁,运行时间短(The application of simulated annealing, genetic algorithm and advanced algorithm of neural network program is the best way to calculate effectively. Compared with violent search, the algorithm
SAGAexp.m
- 遗传算法和模拟退火算法融合,解决遗传算法早熟问题。(Fusion of genetic algorithm and simulated annealing algorithm)
模拟退火算法和遗传算法程序
- 使用MATLAB编写的模拟退火算法和遗传算法的源代码(The source code of the simulated annealing algorithm and genetic algorithm written in MATLAB)
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 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
遗传算法退火算法
- 利用MATLAB实现模拟退火算法的在各实际情况中的应用(The application of simulated annealing algorithm.)
智能优化算法及其应用
- 介绍了模拟退火,遗传算法等优化算法、神经网络、混合优化算法等(The optimization algorithms such as simulated annealing, genetic algorithm, neural network and hybrid optimization algorithm are introduced.)
Annealing algorithm
- 利用退火算法实现无人机路径巡航,但是效果比不上遗传算法。(The annealing algorithm is used to realize the route cruise of UAV.)
30个智能算法模型
- 1-8遗传算法,9 多目标Pareto最优解搜索算法,10 基于多目标Pareto的二维背包搜索算法,11-12免疫算法,13-17粒子群算法,18鱼群算法,19-21模拟退火算法,22-24蚁群算法,25-27神经网络,28 支持向量机的分类,29 支持向量机的回归拟合,30 极限学习机的回归拟合及分类(1-8 genetic algorithm, 9 multi-objective Pareto optimal solution search algorithm, 10 multi-obje
PDPTW
- 采用模拟退火与遗传算法相结合的混合算法解决带时间窗的取送货问题(A hybrid algorithm based on simulated annealing and genetic algorithm is used to solve the delivery and pick-up problem with time windows.)
遗传退火算法fortran实现
- 遗传算法与模拟退火算法的混合算法,用于最优化问题的求解。(The hybrid algorithm of genetic algorithm and simulated annealing algorithm is used to solve the optimization problem.)
Boxing Problem
- 利用遗传算法和模拟退火,解决三维装箱问题,并可图形化展示装箱方案结果(Solving 3-D boxing problem by Genetic Algorithm and Simulated Annealing. In addition, the solution of boxing scheme can be displayed graphically)
111
- 用于求解带时间窗的多车场的配送路径优化问题,即vrp问题,算法是基于模拟退火算法和遗传算法的混合算法(The algorithm is a hybrid algorithm based on simulated annealing algorithm and genetic algorithm, which is used to solve the distribution path optimization problem of multi depot with time window)
粒子群算法
- 粒子群算法,也称粒子群优化算法或鸟群觅食算法(Particle Swarm Optimization),缩写为 PSO, 是近年来由J. Kennedy和R. C. Eberhart等 开发的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种,和模拟退火算法相似,它也是从随机解出发,通过迭代寻找最优解,它也是通过适应度来评价解的品质,但它比遗传算法规则更为简单,它没有遗传算法的"交叉"(Crossover) 和"