UNIX network programming
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Annals of Operations Research - Special issue on Tabu search
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Heuristic Solutions for the Multiple-Choice Multi-dimension Knapsack Problem
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
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Quality adaptation in a multisession multimedia system: model, algorithms, and architecture
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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
A New Heuristic for Solving the Multichoice Multidimensional Knapsack Problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
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Proceedings of the 12th annual conference on Genetic and evolutionary computation
A distributed heuristic solution using arbitration for the MMMKP
AusPDC '10 Proceedings of the Eighth Australasian Symposium on Parallel and Distributed Computing - Volume 107
Information Sciences: an International Journal
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ARCS'12 Proceedings of the 25th international conference on Architecture of Computing Systems
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This paper presents a multiprocessor based heuristic algorithm for the Multi-dimensional Multiple Choice Knapsack Problem (MMKP). MMKP is a variant of the classical 0---1 knapsack problem, where items having a value and a number of resource requirements are divided into groups. Exactly one item has to be picked up from each group to achieve a maximum total value without exceeding the resource constraint of each type. MMKP has many real world applications including admission control in adaptive multimedia server system. Exact solution to this problem is NP-Hard, and hence is not feasible for real time applications like admission control. Therefore, heuristic solutions have been developed to solve the MMKP. M-HEU is one such heuristic, which solves the MMKP achieving a reasonable percentage of optimality. In this paper, we present a multiprocessor algorithm based on M-HEU, which runs in O(T/p+s(p)) time, where T is the time required by the algorithm using single processor, p is the number of processors and s(p), a function of p, is the synchronization overhead. We also present the worst-case analysis of our algorithm, the computation of the optimal number of processors as well as the lower bound of the total value that can be achieved by the heuristic.