1、

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

函数优化和背包问题实验验证了PEA的 有效性.

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2、

This is about 01 knapsack problem dynamic programming algorithm.

这是关于01背包问题的动态规划算法.

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3、

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).

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。

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4、

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

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。

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5、

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

基于Matlab的0-1背包问题的动态规划方法求解

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6、

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.

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。

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7、

0/ 1 Knapsack Problem and Its Solution Methods Study

0/1背包问题及其解法研究

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8、

Study on the application of integer knapsack problem and its algorithm

整数背包问题的应用及其算法研究

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9、

An algorithm for optimizing knapsack problem based on game theory

基于博弈论的背包问题优化算法

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10、

Hybrid genetic algorithm for knapsack problem and its application in power system restoration

背包问题混合遗传算法在电力恢复中的应用

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11、

Two Kinds of Expanding Forms of 0-1 Knapsack Problem and Its Solution Methods

0-1背包问题的两种扩展形式及其解法

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12、

A Method for 0/ 1 Knapsack Problem Which Value Equal with Weight

价值与重量相等的0/1背包问题的一种解法

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13、

The Genetic Algorithm of Solving 0-1's Knapsack Problem and Its Improvement

解0-1背包问题的遗传算法及其改进

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14、

And then we solve different scales of knapsack problem with the new algorithm.

然后利用本文构造的算法分别针对不同规模的背包问题进行了求解。

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15、

Collaborative Particle Swarm Optimization Algorithm for Knapsack Problem Based on Multi-Agent

多代理协作粒子群背包问题求解算法

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16、

Solution to 0/ 1 knapsack problem based on improved ant colony algorithm

基于改进型蚁群算法求解0/1背包问题

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17、

The article established the optimization model for the load restoration, a zero-one knapsack problem with many safe constraints, and designed a modified hybrid genetic algorithm which combine the greedy algorithm and modify genetic algorithm together to resolve it.

文章把电力系统的负荷恢复问题建模为带众多约束条件的0-1背包问题,并设计了一种将贪心算法与改进遗传算法结合起来的改进混合遗传算法来对此问题进行求解。

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