因此，应用该混合智能算法求解0 - 1背包问题是比较有效的。
Therefore, the hybrid intelligence algorithm is effective to solve 0-1 knapsack problems.
基于一维问题的蚂蚁算法，本文将二维矩形件排样问题转化为一维背包问题，然后进行求解。
Two-dimensional stock cutting problem can be settled by solving two one-dimensional knapsack problems, this paper presents a new algorithm based on the ant colony optimization idea.
在求解背包问题时，采用修复函数来修正不可行编码。
When solving the knapsack question, repair function was used to repair unfeasible code.
本文提出了改进的粒子群算法求解背包问题，阐明了该算法求解背包问题的具体实现过程。
In this paper, a modified particle swarm optimization algorithm is presented to solve knapsack problem, and the detailed realization of the algorithm is illustrated.
给出了用这三种算法解决多选择背包问题的基本原理及求解步骤。
The basic principle and step of these three algorithms are given to solve Multiple-choice Knapsack Problem.
本文运用旋转变换法，经有限次变换后，将普通线性规划（LP）问题，化为一个等价的易于求解的连续背包问题。
A rotation transformation method is to be used in changing an ordinary linear programming (LP)problem into an equivalent easily-solved continuous knapsack one after a finite iteration.
针对经典的背包问题，给出一种新的基于蚂蚁优化思想的求解算法。
Based on the ant colony optimization idea, this paper presents a new algorithm for the classical knapsack problem.
试用递归方法设计求解背包问题的算法。
Trial designed recursive algorithm for solving knapsack problem.
求解0 - 1背包问题的精确算法不能在较短时间内求解大规模0 - 1背包问题，使其实用性受到限制。
The precise and approximate algorithms solving 0-1 knapsack problem, precise algorithm could not be used to solve 0-1 knapsack problem in a short time, so it could not be applied extensively.
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.
通过求解背包问题对算法进行验证，实验结果表明所提算法性能较优。
This algorithm is verified by solving knapsack problem, the results of the experiment show that the proposed algorithm can result in better profits.
将串行动态二表算法应用于并行三表算法的设计中，提出一种求解背包、精确的可满足性和集覆盖等背包类NP完全问题的并行三表六子表算法。
A general-purpose parallel three-list six-table algorithm that can solve a number of knapsack-like NP-complete problems is developed in this paper.
为有效解决背包约束条件下的不同问题，我们可以采取不同的方式，以达到求解其最优解。
To solve effectively for the different issues under knapsack constraint, we can adopt a different approach has reached its optimal solution to solve.
为有效解决背包约束条件下的不同问题，我们可以采取不同的方式，以达到求解其最优解。
To solve effectively for the different issues under knapsack constraint, we can adopt a different approach has reached its optimal solution to solve.
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