Results 171 to 180 of about 24,926 (205)

Some computational results on real 0–1 knapsack problems

Operations Research Letters, 1986
Recent algorithms for solving the binary knapsack problem employ LP-based or Lagrangean-based reduction schemes to fix a portion of variables. The author suggests a hybrid reduction scheme which employs Lagrangean-based reduction tests on non-pivot variables.
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Binary Fireworks Algorithm for 0-1 Knapsack Problem

2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), 2019
In this paper, a novel evolutionary algorithm, Binary Fireworks Algorithm (BFWA), is introduced for 0-1 knapsack problem. Firstly, several foundation definitions are proposed, such as binary distance, inversion operator and explosion operator. Besides, BFWA for 0-1 knapsack problem is built by introducing firework explosion, mutation explosion and ...
Junjie Xue, Jiyang Xiao, Jie Zhu
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An Efficient Algorithm for the 0-1 Knapsack Problem

Management Science, 1976
In this note we present an efficient algorithm for the 0-1 knapsack problem and announce the availability of a callable FORTRAN subroutine which solves this problem. Computational results show that 50 variable problems can be solved in an average of 4 milliseconds and 200 variable problems in an average of 7 milliseconds on an IBM 360/91.
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Solving 0/1 Knapsack Problem Using Metaheuristic Techniques

2017 9th IEEE-GCC Conference and Exhibition (GCCCE), 2017
Knapsack problem has gained popularity in the field of combinatorial optimization. In this paper, different types of problem instances were selected. Two each of S-Metaheuristic methods and P-Metaheuristic methods were applied on the problem instances.
Hayatullahi Adeyemo, Moataz Ahmed
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The 0-1 knapsack problem with fuzzy data

Fuzzy Optimization and Decision Making, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kasperski, Adam, Kulej, Michał
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Tackling 0/1 knapsack problem with gene induction

Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2003
We propose a gene induction approach for genetic algorithms. It is more robust compared to the traditional approach in genetic algorithms. The approach was applied to 0/1 knapsack problem. It found near optimal results in all the representative problem instances reported in the literature, while traditional approaches failed in a number of instances ...
A. K. Bhatia, S. K. Basu
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The 0–1 knapsack problem with multiple choice constraints

European Journal of Operational Research, 1978
Abstract In this paper we consider the 0–1 knapsack problem with multiple choice constraints appended. Such a problem may arise in a capital budgeting context where only one project may be selected from a particular group of projects. Thus the problem is to choose one project from each group such that the budgetary constraint is satisfied and the ...
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Upper Bounds and Algorithms for Hard 0-1 Knapsack Problems

Operations Research, 1997
It is well-known that many instances of the 0-1 knapsack problem can be effectively solved to optimality also for very large values of n (the number of binary variables), while other instances cannot be solved for n equal to only a few hundreds. We propose upper bounds obtained from the mathematical model of the problem by adding valid inequalities on
Martello, Silvano, Toth, Paolo
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Threshold Accepting Algorithms For 0–1 Knapsack Problems

1991
In [1] Drexl presented a Simulated Annealing (SA) algorithm for multi-constraint 0–1 knapsack problems (MCKP). Drexl studied 57 different MCKP’s which have been published in the literature. In [2], the new optimization heuristic Threshold Accepting (TA) has been introduced.
Gunter Dueck, Jens Wirsching
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An algorithm for the 0/1 Knapsack problem

Mathematical Programming, 1978
The Knapsack problem (maximize a linear function, subject to a unique constraint, all being in integers), although of thenp-complete type, is a well solved case in combinatorial programming. The reason for this is twofold:(i)an upper bound of the objective function is easy to compute(ii)it is quite simple to construct feasible solutions.
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