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Description: 0-1整数规划有很广泛的应用背景，比如指派问题，背包问题等等，实际上TSP问题也是一个0-1问题，当然这些问题都是NP问题，对于规模较大的问题用穷举法是没有办法在可接受的时间内求得最优解的，本程序只不过是一个练习，得意之处是用递归法把所有解都排列出来。另：胡运权所著的《运筹学基础及应用（第三版）》第97页的例3，我用本程序求解得到的结果是：最优解是x*=(1,0, 0, 0, 0)，最优值是f(x*)=8，但书求得最优解是x*=(1,0, 1, 0, 0)，最优值是f(x*)=4，是不是书中写错了，请大家验证。以下是源程序，大家可以任意使用无版权问题，另外，如果大家有大规模的0-1规划的问题也希望提供给我，谢谢。变量个数至少是3个-0-1 Integer Programming is a very broad background, such as assignment, bags and so on. actually TSP is a 0-1, of course, these problems are NP, for larger problem with the law is not exhaustive approach in an acceptable time frame to obtain the optimal solution. this procedure is only one practice, farewell tour is the method used recursive all of the solutions to show. Another : Hu Yun-author of "operational research, and application base (third edition)" Article 97 of the three cases, I use this procedure to solve the result is : is the optimal solution x = (1,0, 0, 0, 0), the optimal values of f (x*) = 8, but the book is to find the optimum solution x = (1,0, 1, 0, 0), the optimal values of f (x*) = 4, the book is not a mistake, please certification. Following is the source
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Author: wan |
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Description: 应用遗传算法是被认为求解NP难题的有效手段之一,求解物流配送车辆路径优化问题时,在传统遗传算法的基础上,并引入了免疫算法的思想,实验结果表明该算法具有更好的全局和局部搜索能力和收敛速度,可有效地解决物流配送车辆路径优化问题。-Application of genetic algorithms to solve NP is considered an effective means of problem solving to optimize logistics and distribution vehicle routing problem, in the traditional genetic algorithm based on immune algorithm and the introduction of ideas, experimental results show that the algorithm has a better overall and local search ability and convergence speed, which can effectively solve the logistics and distribution VRP.
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Author: 王博文 |
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Description: TSP( Traveling Salesman Problem) is a typical NP complete problem ,genetic algorithm is the perfect method for solving NP complete problem. -TSP (Traveling Salesman Problem) is a typical NP complete problem, genetic algorithm is the perfect method for solving NP complete problem.
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Author: maolei |
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Description: 带有QoS约束的组播路由问题是一个NP完全问题，遗传模拟退火算法是遗传算法和模拟退火算法的一种融合，可以为这类问题提供一个解决方案-With QoS constraint multicast routing problem is an NP-complete problem, genetic simulated annealing algorithm is a genetic algorithm and simulated annealing algorithm as a fusion, you can provide for such a solution
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Author: zhao xian sheng |
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Description: 旅行商（TSP）问题一直以来都是一个NP难问题，旅行商问题（TSP问题）就是一销售商从n个城市中的某一城市出发，不重复地走完其余n-1个城市并回到原出发点，在所有可能的路径中求出路径长度最短的一条。本次软件设计是利用模拟退火算法解决TSP问题，通过该软件设计，对模拟退火算法和旅行商问题有个初步的认识。-Traveling Salesman (TSP) problem has always been a NP hard problem, traveling salesman problem (TSP problem) is a vendor from the n cities in a city of departure, not repeated through the remaining n-1 cities and back to to the original starting point, in all possible path to derive a shortest path length. The software design is the use of simulated annealing algorithm to solve TSP problems, the adoption of the software design, on the simulated annealing algorithm and the traveling salesman problem to have a preliminary understanding.
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Author: 周薇 |
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Description: 基于粒子群优化算法（PSO）的50个城市TSP问题的求解，可推广至类似NP-hard问题。-Based on Particle Swarm Optimization (PSO) of the 50 cities TSP problem solving can be extended to a similar NP-hard problem.
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Author: 孙岩 |
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Description: 基于禁忌搜索算法的50个城市TSP问题的求解，可推广至类似NP-hard问题。-Tabu search algorithm based on the 50 cities TSP problem solving can be extended to a similar NP-hard problem.
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Author: 孙岩 |
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Description: 基于遗传算法的50个城市TSP问题的求解，可推广至类似NP-hard问题。-Genetic Algorithm Based on 50 cities TSP problem solving can be extended to a similar NP-hard problem.
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Author: 孙岩 |
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Description: 本程序是一个用POS来求解NP难问题，比图TSP问题，实际仿真效果证明改算法合理-This procedure is a POS to use NP hard problem to solve than the TSP problem graph, the actual simulation results prove that a reasonable change algorithm
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Author: 杨维 |
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Description: 强大的蚁群算法matlab程序源代码，可以迅速求解vrp等np难问题。-Ant colony algorithm matlab powerful source code, you can quickly solve the difficult problem of np such vrp.
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Author: 储育青 |
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Description: 多目标货物配装问题是一个复杂的组合优化问题，属于NP难问题，本文用混合粒子群算法求解多目标货物配装问题。混合粒子群算法在基本粒子群算法的基础上，通过引进遗传算法中的交叉和变异的策略，避免了陷入局部最优，加快了达到全局最优的收敛速度。此外，本文提出用权重系数来平衡各目标使各目标都能达到相对较优的效果。-Multi-objective loading of goods is a complicated combinatorial optimization problems are NP hard problems, this paper, hybrid particle swarm algorithm to solve multi-objective problem loading cargo. Hybrid Particle Swarm Algorithm in elementary particle swarm optimization based on genetic algorithm through the introduction of crossover and mutation of the strategy to avoid a fall into local optimum, global optimum to achieve accelerated convergence. In addition, this paper, the weight factor used to balance the various objectives so that the objectives can be achieved relatively better results.
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Author: 廖志 |
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Description: application of Bayes detector and NP detector examples with matlab
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Author: mesa177 |
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Description: NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个：
1 确定因素，并建立网络层和控制层模型。
2 创建比较矩阵。
3 按照指标类型针对每列进行规范化。
4 求出每个比较矩阵的最大特征值和对应的特征向量。
5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。
6 将各个特征向量单位化(归一化)，组成判断矩阵。
7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。
8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。
-NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following:
A determining factor, and a network layer and control layer model.
2 create a comparison matrix.
For each of the three types of indicators in accordance with normalized columns.
4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors.
5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements.
6 will each feature vector units of (normalized), to determine the composition of matrix.
7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix.
8 of the weighted super-matrix units of (normalized), seeking the powe
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Author: chen |
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Description: 排课问题是一个有约束的、多目标的组合优化问题，并且已经被证明是一个NP完全问题。
遗传算法借鉴生物界自然选择和自然遗传机制，使用群体搜索技术，尤其是用于处理传统搜索方法难以解决的复杂的和非线性的问题。经过近40年的发展，遗传算法在理论研究和实际应用中取得了巨大的成功，本文将遗传算法用于排课问题的求解，首先讨论了排课问题中的影响因素、主要约束条件、求解目标和难点，并用数学模型完整地描述了排课问题。其次对多个模糊排课目标进行了定量分析，建立了排课优化目标空间。针对排课问题研究了染色体编码方式以及遗传算子的设计，提出了适应度函数的计算方法。最后对排课问题进行了实验。实验结果表明，其过程的目标值跟踪显示，算法稳健趋优，所得结果令人满意。-Course Scheduling problem is a constrained, multi-objective optimization problem, and has proven to be a NP complete problem.
Genetic algorithms reference biosphere and the natural genetic mechanism of natural selection, using the group search technology, particularly the traditional search methods for handling complex and difficult to solve nonlinear problems. After nearly 40 years of development, the genetic algorithm in the theoretical study and practical application was a great success, this paper genetic algorithm for solving the course timetabling problem, first discussed the impact of factors in the course arrangement, the main constraints, to solve goals and difficulties, and a complete mathematical model to describe the course arrangement. Arranging multiple fuzzy goals followed by a quantitative analysis, the optimal target Arranging space. Arranging for the Study of the chromosome coding and genetic operators design, proposed fitness function is calculated. Finally, the co
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Author: 张林杰 |
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Description: 一个基于粒子群优化算法源码,（PSO）的50个城市TSP问题的求解，可推广至类似NP.-A source code based on particle swarm optimization, (PSO) of the 50 cities TSP problem solving, can be extended to similar NP.
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Author: 抛弃 |
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Description: 基于模拟退火的粒子群算法，基于自然选择的粒子群算法，基于杂交的粒子群算法-Based on simulated annealing particle swarm algorithm, based on natural selection, particle swarm optimization, particle swarm optimization based on hybrid
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Author: Scott |
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Description: A SA arithmetic desolve a NP problem of TSP。The result is not bad
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Author: 李宜鹏 |
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Description: 详细的讲述了贪心算法和遗传算法相结合去求解NP难题，背包问题。-Greedy algorithm described in detail and the combination of genetic algorithms to solve the NP problem, knapsack problem.
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Author: liulei |
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Description: Neyman_Pearson 根据NP原理对软木塞进行分类的完整函数。部分如下：
function []=Neyman_Pearson(opt)
从excel中读进数据
[source,txt]=xlsread( CORK_STOPPERS.xls , Data ) 读取CORK_STOPPERS.xls文件里Data sheet里的数据，
数据存放在source变量里，文本存放在txt变量里
data=[source(:,3:4),source(:,9)] 将用到的三个特征值存入矩阵Data中
data1=data(1:100,:) 无缺陷样本集
data2=data(101:150,:) 有缺陷样本集
计算两类样本的均值和协方差矩阵-Neyman_Pearson
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Author: guangdong |
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Description: 基于遗传算法的投影寻踪代码，提供大家下载，方便查阅-【研学堂】【代码】投影寻踪代码，请验用！！
function Qa=Project_Pursuit(X,a,Alpha)
输入参数列表
X 本指标矩阵，n×p的矩阵，每一行为一个样本，
Xij表示第i行第j列指标，X是否已经归一化均可
a 投影向量，1×p的矩阵，元素取值范围-1～1，要求其元素平方和等于1
Alpha 窗口半径系数，典型取值0.1
输出参数列表
Qa 投影指标函数
第零步：对a的预处理
b=sqrt(sum(a.^2))
a=a/b
第一步：归一化处理
[n,p]=size(X)
x=zeros(n,p)
Xjmax=max(X)
Xjmin=min(X)
for i=1:n
x(i,:)=(X(i,:)-Xjmin)./(Xjmax-Xjmin)
end
第二步：构造投影指标值
Z=zeros(n,1)
for i=1:n
Z(i)=sum(a.*x(i,:))
end
第三步：计算投影指标函数
计算类间类内矩阵散度
meanZ=mean(Z)
Sa=0
for k=1:n
sa=(Z(i)-meanZ(i)).^2
Sa=Sa+sa
Sa=sqrt(Sa/n)
end
R=Alpha*Sa 窗口半径
Da=0
for k=1:n
rik=abs(Z(i)-Z(k))
if R>rik
Da=Da+rik Da=Da+R-rik
end
end
Qa=Sa*Da
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Author: 余文乐 |
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