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
贪婪算法解决背包
- 运用贪婪算法能够很好解决0/1背包问题,这是我编的一个小程序,运行过很好。-greedy algorithm can be used to solve a very good 0 / 1 knapsack problem, this is my part of a small program running very good.
0-1bagsourcecode
- 0-1背包问题的C++源代码: 设有一个背包,可以放入重量为S。现有n件物品,重量分别为S1,S2,···,Si (0<i<n+1)均为正数,从这n件物品中
0-1wenti
- 该程序是一个用动态规划法解决0-1背包问题的小程序,可以为学习动态规划法遇到困难的同学提供帮助-the program is a dynamic programming solution 0-1 knapsack problem of small procedures, to study the dynamic programming method difficulties encountered by students for help
0-1beibao
- 0-1背包问题的动态规划求解算法, 0-1背包不同于背包问题-0-1 knapsack problem of the dynamic programming algorithm, 0-1 knapsack different from the knapsack problem
0-1snap
- 算法设计与分析 第五章王晓东版的 回溯法的0-1背包问题 -algorithm design and analysis of the fifth edition of Wang Xiaodong Zhang law retroactively 0-1 knapsack problem
0-1package
- 遗传算法求解0-1背包问题,包含问题的求解步骤。-Genetic Algorithm 0-1 knapsack problem, including steps to solve the problem.
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
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)