Solving multidimensional knapsack problems with generalized upper bound constraints using critical event tabu search

Li, Vincent C.; Curry, Guy L.

doi:10.1016/j.cor.2003.08.021

Cited by 26 publications

(12 citation statements)

References 37 publications

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“…Several of their results for these instances were considerably improved by Vasquez [35,36], through a tabu-search algorithm. After that, many studies proposed new tabu-search methods for the MKP [37][38][39]. In GA's field, CBGA has influenced a series of studies concerning new GA's operators and fitness-landscapes analysis [40][41][42][43][44], and still is used as reference for more recent works with univariate EDAs [45,46,17,18], particle swarm optimization [47,48], differential evolution [49].…”

Section: The Multidimensional Knapsack Problemmentioning

confidence: 99%

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

2014

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Section: The Multidimensional Knapsack Problemmentioning

confidence: 99%

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

2014

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“…The value-dependent and correlated offers of the rule selection problem make it similar to the N P-hard 0-1 multidimensional knapsack problem with value-dependent items, and is unlikely to be solved in pseudo-polynomial time [32,40]. Polynomial and pseudopolynomial time heuristics must restrict the problem to a single linear constraint which makes them impractical to directly apply to the rule selection problem [19].…”

Section: Challengesmentioning

confidence: 99%

In-Network Filtering of Distributed Denial-of-Service Traffic with Near-Optimal Rule Selection

Sisodia

Jiao

2020

Proceedings of the 15th ACM Asia Conference on Computer and Communications Security

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A recent trend to mitigate large-scale distributed denial-of-service (DDoS) attacks is in-network filtering, where victims can deploy traffic-filtering rules in networks other than their own. However, given multiple constraints, such as the number of rules a victim can afford to deploy, the set of rules that DDoS defense entities allow a victim to deploy, and the amount of collateral damage to limit, the selection of rules has a large impact on the efficacy of an in-network filtering solution. In this paper, we introduce a new, offer-based operational model for in-network DDoS defense and formulate the N P-hard rule selection problem for this model. We then design an algorithm that overcomes the fundamental limitations of the classical ACO framework and transform it with several key changes to make it applicable to the domain of in-network DDoS defense. Finally, we use a real-world-based Internet routing topology and two real-world DDoS traces, along with one synthetic trace that follows the attack distribution of the recent Mirai DDoS attack, to evaluate the efficacy and runtime of our algorithm against four other rule selection algorithms, and show our algorithm is near-optimal. CCS CONCEPTS • Networks → Denial-of-service attacks; • Theory of computation → Bio-inspired optimization.

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“…In [27], a critical event tabu search heuristic was presented to solve the multidimensional knapsack problems (MKPs) with generalized upper bound constraints. Osorio et al [41] considered a combined cutting and surrogate constraint analysis strategy for MKPs, see also [19].…”

Section: Introductionmentioning

confidence: 99%

On a New Constraint Reduction Heuristic Using Improved Bisection Method for Mixed Integer Linear Programming

Akdemir

2021

ijaa

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In this study, we develop a surrogate relaxation-based procedure to reduce mixed-integer linear programming (MILP) problem sizes. This technique starts with one surrogate constraint which is a nonnegative linear combination of multiple constraints of the problem. At this initial step, we calculate optimal Lagrangian multipliers from LP relaxation of the problem and use them as initial surrogate multipliers. We incorporate the improved bisection method (IBM) (B. Gavish, F. Glover, and H. Pirkul, Surrogate Constraints in Integer Programming, J. Inform. Optim. Sci. 12(2) (1991), 219-228.) into our algorithm. This simple heuristic algorithm is designed to iteratively generate a new surrogate cut that is to guarantee to satisfy the most violated two constraints of the corresponding iteration. The performance of the heuristic is tested using both some problems from the OR libraries and randomly generated ones.

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Solving multidimensional knapsack problems with generalized upper bound constraints using critical event tabu search

Cited by 26 publications

References 37 publications

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

In-Network Filtering of Distributed Denial-of-Service Traffic with Near-Optimal Rule Selection

On a New Constraint Reduction Heuristic Using Improved Bisection Method for Mixed Integer Linear Programming

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