搜索资源列表
dossier
- For the incomplete methods, we kept the representation of the queens by a table and the method of calculation to determine if two queens are in conflict, which is much faster for this kind of problems than the representation by a matrix. heuristics
Guided_Local_Search_to_the_TSP
- 与禁忌搜索动态修改邻域结构的方法不同, GLS的基本原则是通过不断改变搜索空间的地形(landscape)来帮助搜索过程逐步移出局部极值的, 也就是说搜索过程中解结构和邻域结构将保持不变, 而目标函数将被动态修改, 以使得当前的局部极值不再具有局部最优性。-Guided Local Search sits on top of local search heuristics and has as a main aim to guide these procedures in exploring e
maugis
- 模拟退火和对称 *欧几里德旅行商问题。 * *为基础的解决办法的本地搜索启发式 *非过境道路和近邻 -/* * Simulated annealing and the Symetric * Euclidian Traveling Salesman Problem. * * Solution based on local search heuristics for * non-crossing paths and nearest neighbor
FEC
- 聚类算法中的启发式算法中的FEC算法，可以处理复杂网络的问题。-Clustering algorithm heuristics in the FEC algorithm can handle complex network problems.
nQueens
- 人工智能课程实验：使用启发式搜索求解皇后问题。启发式搜索是利用度量作为指南的一种搜索方法。皇后问题是计算机界的经典问题，n皇后问题为把n个皇后放入一个n*n的棋盘中，使皇后两两不在同一行，同一列以及同一斜线中。求解n皇后问题的算法众多，主要有回溯法，构造法，概率算法以及本实验所用的启发式搜索方法等。方法不同，求解问题所产生的时间效率也大不相同，本实验主要对比构造法与启发式搜索方法的效率。 -Artificial Intelligence Curriculum Experiment: Usin
svm4
- -s svm类型：SVM设置类型(默认0) 0 -- C-SVC 1 --v-SVC 2 – 一类SVM 3 -- e -SVR 4 -- v-SVR -t 核函数类型：核函数设置类型(默认2) 0 – 线性：u v 1 – 多项式：(r*u v + coef0)^degree 2 – RBF函数：exp(-r|u-v|^2) 3 –sigmoid：tanh(r*u v + coef0) -d degree
ACO-P-Tabu
- This refers to ACO which is a meta heuristics and use this in Tabu search algorithm.
fiifteen_puzzle
- Program implements graphs algorihtms to solve fifteen puzzle problem (http://en.wikipedia.org/wiki/Fifteen_puzzle). There are few methods BFS - Hamming and Manhattan heuristics, Breadth-first search and Depth-first search. You can implement A* algori
TSP_TS
- Solution for Travelling Salesman Problem by using Tabu Search heuristics. Archive contains sources and some data to test the appllication. As an input, we take the coordinates of cities (x,y) and then transform them into distances matrix. All co
TSP-Heuristics
- Another version of TSP Travelling Salesman Problem Heuristic. We included the MST-Heuristik (Minimum Spaning Tree) as well as the Christofides-Heuristik. Both solve the TSP Problem.
SOA
- Seeker optimization algorithm(SOA)is an ovelpopulation-based heuristics tochastic search algorithm which is based on the concep to f simulating the act of human searching In the SOA,the search direction is determined by seeker’segotistic behavior,alt
queen
- N皇后问题是算法中回溯法应用的一个经典案例 回溯算法也叫试探法，它是一种系统地搜索问题的解的方法。回溯算法的基本思想是：从一条路往前走，能进则进，不能进则退回来，换一条路再试。 -N queens problem is backtracking algorithm applied a classic case of backtracking algorithm, also called heuristics, which is a systematic method for solvi
Cuckoo-algorithm
- 本程序介绍了一种新型的生物智能启发式算法-布谷鸟算法，用于对求解问题的优化分析。-This program introduces a new type of biological intelligence heuristics - Cuckoo algorithm used to solve the problem of optimization analysis.
heuristics
- Heuristic optimization for Matlab
VNV_VariableNeighbourhoodSearch
- Local Search Meta-heuristics that explores the solution field trough systematic neighborhood structures switches, gradatively exploring distant neighbors in search for the optimal solution
Functions
- Benchmark fucntions often used in testing of optimization heuristics.
optimization-algorithm
- 在运筹学基础中，有讲解许多算法，在实际中也会用的到，在这编了几个程序，有凸函数、加布探索法、牛顿法，多数情况下是通用的，只需改几个参数，换下函数即可。-In Operations Research Foundation, there are many algorithms to explain which will be used to do something in practice.There are series of few procedures, including convex f
MODA
- 我很高兴与你们分享我最近在这页上的工作。是的，你的猜测是正确的，这是一种新的算法，但是这次我已经开发了三种算法来解决三种不同类型的优化问题。 蜻蜓算法(DA)算法的主要灵感来源于静态和动态的变暖行为。这两种变暖行为非常类似于使用元启发式进行优化的两个主要阶段:勘探和开发。蜻蜓能在静止的蜂群中产生亚群，并飞过不同的区域，这是探索阶段的主要目标。然而，在静止的蜂群中，蜻蜓在更大的群中飞行，沿着一个方向飞行，这在开发阶段是有利的。-I am pleased to share my recent w
遗传算法
- 利用遗传算法计算目标函数极值，遗传算法与传统的优化方法（枚举，启发式等）相比较，以生物进化为原型，具有很好的收敛性，在计算精度要求时，计算时间少，鲁棒性高等都是它的优点。(Using the genetic algorithm to calculate the extremum of a objective function. Compared with the traditional optimization methods (enumeration, heuristics, etc.), g
ADOSH_1.2_mcode_7_3_2018
- Analysis and Design Optimization of truss Structures using meta-Heuristics (ADOSH).