Tabu Search
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Building an Adaptive Multimedia System using the Utility Model
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Quality adaptation in a multisession multimedia system: model, algorithms, and architecture
Quality adaptation in a multisession multimedia system: model, algorithms, and architecture
Combinatorial Auctions: A Survey
INFORMS Journal on Computing
Towards the real time solution of strike force asset allocation problems
Computers and Operations Research
Computers and Operations Research
A Reactive Local Search-Based Algorithm for the Multiple-Choice Multi-Dimensional Knapsack Problem
Computational Optimization and Applications
Solving the multidimensional multiple-choice knapsack problem by constructing convex hulls
Computers and Operations Research
Computers and Operations Research
A column generation method for the multiple-choice multi-dimensional knapsack problem
Computational Optimization and Applications
Development of core to solve the multidimensional multiple-choice knapsack problem
Computers and Industrial Engineering
Problem reduction heuristic for the 0-1 multidimensional knapsack problem
Computers and Operations Research
A New Heuristic for Solving the Multichoice Multidimensional Knapsack Problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
First-level tabu search approach for solving the multiple-choice multidimensional knapsack problem
International Journal of Metaheuristics
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An adaptive memory projection (referred as AMP) method is developed for multidimensional knapsack problems (referred as the MKP) with generalized upper bound constraints. All the variables are divided into several generalized upper bound (referred as GUB) sets and at most one variable can be chosen from each of the GUB sets. The MKP with GUBs (referred as the GUBMKP) can be applied to many real-world problems, such as capital budgeting, resource allocation, cargo loading, and project selection. Due to the complexity of the GUBMKP, good metaheuristics are sought to tackle this problem. The AMP method keeps track of components of good solutions during the search and creates provisional solution by combining components of better solutions. The projection method, which can free the selected variables while fixing the others, is very useful for metaheuristics, especially when tackling large-scale combinatorial optimization. In this paper, the AMP method is implemented by iteratively using critical event tabu search as a search routine, and CPLEX in the referent optimization stage. Variables that are strongly determined, consistent, or attractive, are identified in the search process. Selected variables from this pool are fed into CPLEX as a small subproblem. In addition to the diversification effect within critical event tabu search, the pseudo-cut inequalities and an adjusted frequency penalty scalar are also applied to increase opportunities of exploring new regions. This study conducts a comprehensive sensitivity analysis on the parameters and strategies used in the proposed AMP method. The computational results show several variants of the AMP method outperforms the tight oscillation method in the literature of GUBMKP. On average, consistent variables tend to perform best as a pure strategy. A pure strategy equipped with local search can lead into even better results. Last but not least, testing different types of variables in the referent optimization stage before selecting just one of the pure strategies is found to be very helpful.