An approximate algorithm for multidimensional zero-one knapsack problems
Management Science
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Dynamic tabu list management using the reverse elimination method
Annals of Operations Research - Special issue on Tabu search
An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Using surrogate constraints in genetic algorithms for solving multidimensional knapsack problems
Advances in computational and stochastic optimization, logic programming, and heuristic search
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Meta-Heuristics: Theory and Applications
Meta-Heuristics: Theory and Applications
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Practical Partners: MicroComputers and the Industrial Engineer
Practical Partners: MicroComputers and the Industrial Engineer
Dynamic Programming
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A promising solution approach called Meta-RaPS is presented for the 0-1 Multidimensional Knapsack Problem (0-1 MKP). Meta-RaPS constructs feasible solutions at each iteration through the utilization of a priority rule used in a randomized fashion. Four different greedy priority rules are implemented within Meta-RaPS and compared. These rules differ in the way the corresponding pseudo-utility ratios for ranking variables are computed. In addition, two simple local search techniques within Meta-RaPS improvement stage are implemented. The Meta-RaPS approach is tested on several established test sets, and the solution values are compared to both the optimal values and the results of other 0-1 MKP solution techniques. The Meta-RaPS approach outperforms many other solution methodologies in terms of differences from the optimal value and number of optimal solutions obtained. The advantage of the Meta-RaPS approach is that it is easy to understand and easy to implement, and it achieves good results.