搜索资源列表
背包算法
- 高级算法中的背包问题求解，算法简便高效，主要解决动态规划0-1背包问题-senior algorithm to solve the knapsack problem, the algorithm is simple and efficient, dynamic programming solution 0-1 knapsack problem
QEA-solving-0-1-Knapsack-problem
- 主要是利用QEA解决0-1背包问题的程序-It isuseful to solving the problem of 0-1 Knapsack using QEA
0-1
- 用于解决0/1 背包问题动态规划详解及C代码，清晰明确，简单易学-Planning the Xiangjie and C code used to solve the 0/1 knapsack problem dynamic, clear, and easy to learn
0-1
- 我自己用C写的一个用分支界限法实现0-1背包问题，比较简便实用，而且易懂，比回溯法有明显的优势-I have written in C, a branch and bound method 0-1 knapsack problem is relatively simple and practical, and easy to understand, there are obvious advantages than backtracking
ts-solve-0-1-knapsack-a-info
- 用禁忌搜索解决0-1背包问题，及一些关于禁忌搜索优化和并行处理的资料-Tabu search to solve 0-1 knapsack problem, and some information on tabu search optimization and parallel processing of data
0-1-Knapsack-problem
- 本次实验选择0-1背包问题作为题目，通过使用动态规划、回溯法和分支定界法等算法来求解该问题，从而进一步的了解各种算法的原理、思路及其本质，深化对算法的了解，锻炼自己对各种算法的分析和使用，熟悉软件底层算法和界面编程。-The 0-1 knapsack problem was chosen as the subject, through the use of dynamic programming, backtracking and branch and bound method algorit
0-1
- 用动态规划思路去解答经典的0-1背包问题，已成功通过调试-Using dynamic programming ideas to answer the classic 0-1 knapsack problem, has successfully passed the debugging
the-problems-of-0-1-package
- 0-1背包问题在0 / 1背包问题中，需对容量为c 的背包进行装载。从n 个物品中选取装入背包的物品，每件物品i 的重量为wi ，价值为pi 。对于可行的背包装载，背包中物品的总重量不能超过背包的容量，最佳装载是指所装入的物品价值最高-the problems of 0-1 package
0-1-backpack
- 本代码提供0-1背包问题的动态规划解法，适用于背包容量是整数类型-The code provides 0-1 knapsack problem dynamic programming solution for the backpack capacity is an integer type
0-1-knapsack-problem--
- 算法分析与设计（王晓东版）的0-1背包问题的改进算法的java代码实现，实现背包问题的求解-Algorithm analysis and design (Wang Xiaodong version) algorithm to improve the 0-1 knapsack problem is java code
Backtracking-0-1
- 0-1背包问题的回溯法求解,0-1背包是在M件物品取出若干件放在空间为W的背包里，求出获得最大价值的方案。算法设计 回溯的思想。-Backtracking 0-1 knapsack problem solving 0-1 knapsack is removed in several pieces on items M space W backpack, determined to get the maximum value of the program. Backtracking algorit
0-1-package-question
- 0——1背包问题的解决，注重动态规划的使用，简单快捷，方便解决0-1规划问题的解决-the question of 0-1 package problem
simulated annealing algorithm
- 模拟退火算法的应用很广泛，可以较高的效率求解最大截问题(Max Cut Problem)、0-1背包问题(Zero One Knapsack Problem)、图着色问题(Graph Colouring Problem)、调度问题(Scheduling Problem)等等。(Simulated annealing algorithm is widely used, can be more efficient to solve the maximum Problem Cut (Max), 0-1
c1
- 动态规划解决背包问题 列出所有可能情况并进行求值 适用于较小数据测试(Dynamic programming to solve knapsack problem)
背包
- 给定n种物品和一背包。物品i的重量是wi，体积是bi，其价值为vi，背包的容量为c，容积为d。问应如何选择装入背包中的物品，使得装入背包中物品的总价值最大？在选择装入背包的物品时，对每种物品i只有两种选择，即装入背包或者不装入背包。不能将物品i装入背包多次，也不能只装入部分的物品i。试设计一个解此问题的动态规划算法，并分析算法的计算复杂性。(Given n items and a knapsack. The weight of the item I is wi, the volume is Bi
AOC_limit
- 使用matlab实现的蚁群算法，解决0 1背包问题为例解决组合优化问题(ant colony optimization (ACO) implement by matlab, use to solve 0/1 bagging problem)
GeneticAlgorithm
- 使用传统的遗传算法解决0-1背包问题，其中使用的是轮盘选择、最简单的随机交叉变异(Using traditional genetic algorithm to solve the 0-1 knapsack problem)
test.py
- 通过遗传算法解决0-1背包问题，以选择办事处为背景(solve the package problem through genetic algorithm)
采用基于粒子群的多目标优化算法解决背包问题
- 多目标优化问题与粒子群算法的结合，以解决0-1背包问题(The multi-objective optimization problem is combined with particle swarm optimization to solve the 0-1 knapsack problem)
Chapter9
- 用分支界限法来解决0-1背包问题，包含算法分析(Branch and bound method for solving 0-1 knapsack problem)