背包问题

基于Matlab的0-1背包问题的动态规划方法求解

DP Algorithm of Solving 0-1 's Knapsack Problem Based on Matlab

解0-1背包问题的混合编码贪婪DE算法

Mixed Coding Greedy Differential Evolution Algorithm for 0-1 Knapsack Problem

背包问题模型的MATLAB程序实现

MATLAB Program Implement of the Knapsack Problem

0-1背包问题和背包问题是一类经典的NP困难问题。

0-1 knapsack problems and knapsack problems are a classical NP hard problems .

背包问题是一个典型的NP完全问题。

Knapsack problem is a typical NP complete problem .

积性效用函数的度量函数优化和背包问题实验验证了PEA的有效性。

The function optimization and knapsack problem show the effectiveness of PEA .

本文应用整数背包问题有关理论，对CD音轨智能编辑转录问题进行了讨论，提出了一个数学模型及相应的递归算法。

This paper discusses the problem of intelligently editing and recording CD tracks by using the related theory of integer knapsack problem .

该算法能快速有效地求解可分离连续凸二次背包问题的最优解，算法的时间复杂度和空间复杂度都是O（n），都比经典算法节约很多。

The computational complexity on time and space of the proposed algorithm are both O ( n ), which are both smaller than those of the classical algorithms .

对一类优化问题&背包问题（0-1KnapsackProblem）的求解过程进行了分析，得到了不变决策变量集合，为化简对问题求解的算法奠定了数学基础。

To analyze result process of 0-1 knapsack problem ( a kind of optimization problem ), obtained the set of immovability decision-making variables . So , establish the mathematics foundation for being simple the algorithm to solve the problem .

将多个约束变量的ILP问题转化为一个约束条件的背包问题。

Transfer the multiple constraints ILP problem into one dimension knapsack problem .

对于背包问题现有许多不同的求解方法。文中给出基于PSO的背包问题的一种新的求解方法。

Many various ways exist to resolve the Knapsack Problem , and this paper provides a new method called PSO to resolve it .

从复杂度理论角度出发，讨论了如何用量子搜索算法加速背包问题等NP完全问题的求解。

How to use Grover 's quantum searching algorithm to speed up the knapsack problem was talked about based on computational complexity theory .

在著名的多目标优化遗传算法NSGA-II中，引入邻域搜索机制，并将其应用于多目标0-1背包问题的求解。

In the famous algorithm NSGA-II , the neighborhood search is introduced .

多目标0/1背包问题MOEA求解中的修复策略

Repair Strategies for Multiobjective 0 / 1 Knapsack Problem in MOEA

UEP的优化问题可以重新建模为带额外约束的多选择背包问题。

The optimization problem of UEP could be reformulated as a knapsack problem with additional constraints .

多目标0-1背包问题是一个NP-complete的多目标优化问题，基于群体搜索机制的遗传算法非常适合多目标优化问题的求解。

Multi-objective 0-1 knapsack problem is an NP-complete multi-objective optimization problem . Population-based genetic algorithm is well-suited for multi-objective optimization problems .

0/1背包问题是实际当中经常遇到的一类经典NP-hard组合优化问题之一。

The 0 / 1 knapsack problem is a classic NP-hard problem in the combinational optimization 1 which is often encountered in practice .

多维0-1背包问题同时还是一个典型的NPC问题，对背包问题的研究无论在实际应用还是在理论研究中都有着重要意义。

Multi-dimensional 0-1 knapsack problem is also one of a class ' of typical NPC problem . It has important meanings in practice and in theory to study it .

求解多目标背包问题的仿真结果表明，所提算法可以快速收敛到较好的Pareto前沿，有很强的鲁棒性。

The empirical results on the multi-objective knapsack problem show that above algorithm is able to converge on the better Pareto front quickly and has a strong robustness .

扼要介绍多目标优化的Pareto最优性概念，研究搜索多目标01背包问题Pareto最优解集的快速遗传算法（FPGA：fastParetogeneticalgorithms）。

This paper briefly describes Pareto optimality in multiobjective optimization , investigates a kind of fast Pareto genetic algorithms ( FPGA ) searching Pareto optima of multiobjective 0 / 1 knapsack problems .

数值实验表明，引入邻域搜索机制的NSGA-II算法在求解多目标0-1背包问题时表现出更好的性能。

The numerical experimental results show that NSGA-II with the neighborhood search can outperform NSGA-II applied to multi-objective 0-1 knapsack problems .

本文发现，基于字典序的max-min公平性问题可以利用多维多选择背包问题予以描述。

We also found it is convenient to model the lexicographical max-min fairness problem among multiple sinks with the Multi-dimensional Multiple choice Knapsack Problem ( MMKP ) .

将二次规划中K-T点复杂性问题转化为线性互补复杂性问题，并结合背包问题得出二次规划是NP难问题。

The computational complexity problem of K-T point in quadratic programming is transformed into linear complementarity 's computational complexity problem , and combining with knapsack problem , we obtained that quadratic programming is an NP-hard problem .

以0-1背包问题和TSP问题为例进行仿真实验，实验结果表明，改进的量子遗传算法比传统的遗传算法和量子遗传算法有更好的收敛速度和优化结果。

The experiment takes 0-1 knapsack and TSP problem of combinatorial optimization as example , the experimental results show that the improved quantum genetic algorithm is better than the traditional genetic algorithms and quantum genetic algorithm , both in convergence speed and optimization results . 3 .

本文对单约束线性整数规划（ILP，背包问题）的特性进行了分析，通过剪去无效变量对问题进行简化并设计了问题的一种新算法&降维递归算法，全文共分四章。

In this paper , we analyzed some property for the single restrict integer linear programming ( Knapsack problem ), Cut the invalided variable and simplify the problem . Designed a new algorithm & reduce dimension and Recursive algorithm .

提出了基于MIMD-DM的收缩背包问题的并行分枝界限算法；

And then the parallel branch and bound algorithm of CKP is presented , which is based on MIMD-DM model .

引入了列生成技术，在推导多原材二维Guillotine优化下料模型求解的列生成数学形式的基础上，给出了列生成迭代求解算法，研究并分析了二维背包问题和一维背包问题的求解算法；

On the base of the column generation formulas that are deduced to solve multi-material two-dimensional Guillotine cutting-stock model , the column generation algorithm is brought forward by using column generation technology ; The solution of two-dimensional knapsack problem and one-dimensional knapsack problem is studied ;

函数优化和0-k背包问题实验表明，与量子进化算法和传统遗传算法相比，概率进化算法在适用范围、搜索能力和收敛速度上有明显的优势。

The function optimization and 0-k knapsack problem experiments show that PEA has apparent superior in application area , searching capability and computation time compared with QEA and canonical genetic algorithm ( CGA ) .

针对一类组合优化问题-多维0-1背包问题（MKP），属于NP-难问题，提出一种能减少求解难度的方法&可行域替代解法。

In this paper , we raise a method of reducing the solving difficulty-the creative method of non-equivalence single restrict , aiming at a kind of combination and optimized problem - multi-dimension 0-1 knapsack problem ( also called the NP - hard problem ) .

该策略首先基于动态规划算法和分枝定界算法通过求解包含多个树背包问题（TKP）的孤岛建立和孤岛合并问题而得到初始孤岛组成，然后通过可行性校验和调节得到最终孤岛划分方案。

Initial optimum island partition scheme is gained through island partition procedures including multiple tree knapsack problems ( TKP ) and island combination procedures based on dynamic programming algorithm and branch and bound algorithm . The final island partition scheme is obtained after feasibility checking and adjustment .