Tabu Search
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Lagrangian Cardinality Cuts and Variable Fixing for Capacitated Network Design
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Exploring relaxation induced neighborhoods to improve MIP solutions
Mathematical Programming: Series A and B
Adaptive memory search for multidemand multidimensional knapsack problems
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A Local-Search-Based Heuristic for the Demand-Constrained Multidimensional Knapsack Problem
INFORMS Journal on Computing
A hybrid approach for the 0-1 multidimensional knapsack problem
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
The power of semidefinite programming relaxations for MAX-SAT
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Conflict analysis in mixed integer programming
Discrete Optimization
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The Multidimensional Knapsack/Covering Problem (KCP) is a 0---1 Integer Programming Problem containing both knapsack and weighted covering constraints, subsuming the well-known Multidimensional Knapsack Problem (MKP) and the Generalized (weighted) Covering Problem. We propose an Alternating Control Tree Search (ACT) method for these problems that iteratively transfers control between the following three components: (1) ACT-1, a process that solves an LP relaxation of the current form of the KCP. (2) ACT-2, a method that partitions the variables according to 0, 1, and fractional values to create sub-problems that can be solved with relatively high efficiency. (3) ACT-3, an updating procedure that adjoins inequalities to produce successively more constrained versions of KCP, and in conjunction with the solution processes of ACT-1 and ACT-2, ensures finite convergence to optimality. The ACT method can also be used as a heuristic approach using early termination rules. Computational results show that the ACT-framework successfully enhances the performance of three widely different heuristics for the KCP. Our ACT-method involving scatter search performs better than any other known method on a large set of KCP-instances from the literature. The ACT-based methods are also found to be highly effective on the MKP.