Variable neighborhood search for the heaviest -subgraph

Brimberg, Jack; Mladenović, Nenad; Urošević, Dragan; Ngai, Eric W.T.

doi:10.1016/j.cor.2008.12.020

Cited by 50 publications

(62 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The choice of this particular heuristic for our protocol is motivated by the work of Brimberg et al who report in [25] that VNS consistently achieved the best results in solving the heaviest ksubgraph problem. The heuristics against which Brimberg et al tested VNS include [26]: multi-start local search, tabu search, and scatter search.…”

Section: Vns Heuristicmentioning

confidence: 99%

Decentralized group formation

Chmielowiec

Voulgaris

Steen

2014

J Internet Serv Appl

View full text Add to dashboard Cite

Imagine a network of entities, being it replica servers aiming to minimize the probability of data loss, players of online team-based games and tournaments, or companies that look into co-branding opportunities. The objective of each entity in any of these scenarios is to find a few suitable partners to help them achieve a shared goal: replication of the data for fault tolerance, winning the game, successful marketing campaign. All information attainable by the entities is limited to the profiles of other entities that can be used to assess the pairwise fitness. How can they create teams without help of any centralized component and without going into each other's way? We propose a decentralized algorithm that helps nodes in the network to form groups of a specific size. The protocol works by finding an approximation of a weighted k-clique matching of the underlying graph of assessments. We discuss the basic version of the protocol, and explain how dissemination via gossiping helps in improving its scalability. We evaluate its performance through extensive simulations.

show abstract

Section: Vns Heuristicmentioning

confidence: 99%

Decentralized group formation

Chmielowiec

Voulgaris

Steen

2014

J Internet Serv Appl

View full text Add to dashboard Cite

show abstract

“…In addition, two additional moves are considered to provide more chance to obtain a schedule with lower makespan including swapping ( 4 N considers moving an internal operation to the very beginning or to the very end of a block.  Neighborhood Structure 5 N : Nowicki and Smutnicki [18] introduced the smallest neighborhood area using neighborhood structure 5 N in which only the first two operations or the last two operations of a critical block are the candidates for swap operation.  Neighborhood Structure 6 N : The extension of all previously described neighborhood structures is proposed by Balas and Vazacopoulos [19].…”

Section: Neighborhood Structuresmentioning

confidence: 99%

“…N are incorporated with different strategies for RecursiveShake and RecursiveLocalSearch procedures which are presented in Equations (1) and (2), respectively. Furthermore, the neighborhood structure 5 N is considered as a nested neighborhood structure for RecursiveLocalSearch when it cannot find a better solution.…”

Section: Neighborhood Structuresmentioning

confidence: 99%

See 1 more Smart Citation

Recursive Variable Neighborhood Search

Raeesi¹,

Kobti²

2014

IJMLC

View full text Add to dashboard Cite

show abstract

“…In addition to its theoretical significance as a difficult combinatorial problem, MDP is notable for its ability to formulate a number of practical applications: location of undesirable or mutually competing facilities [11], decision analysis with multiple objectives [36], composing jury panels [28], genetic engineering [33], medical and social sciences [27], and product design [18]. During the past three decades, MDP has been studied under many different names such as maxisum dispersion [26], MAX-AVG dispersion [39], edge-weighted clique [1,31], remote-clique [9], maximum edge-weighted subgraph [30], and dense k-subgraph [8,12].…”

Section: Introductionmentioning

confidence: 99%

A hybrid metaheuristic method for the Maximum Diversity Problem

Hao

2013

European Journal of Operational Research

View full text Add to dashboard Cite

The Maximum Diversity Problem (MDP) consists in selecting a subset of m elements from a given set of n elements (n > m) in such a way that the sum of the pairwise distances between the m chosen elements is maximized. We present a hybrid metaheuristic algorithm (denoted by MAMDP) for MDP. The algorithm uses a dedicated crossover operator to generate new solutions and a constrained neighborhood tabu search procedure for local optimization. MAMDP applies also a distance-and-quality based replacement strategy to maintain population diversity. Extensive evaluations on a large set of 120 benchmark instances show that the proposed approach competes very favorably with the current state-of-art methods for MDP. In particular, it consistently and easily attains all the best known lower bounds and yields improved lower bounds for 6 large MDP instances. The key components of MAMDP are analyzed to shed light on their influence on the performance of the algorithm.

show abstract

Variable neighborhood search for the heaviest -subgraph

Cited by 50 publications

References 16 publications

Decentralized group formation

Decentralized group formation

Recursive Variable Neighborhood Search

A hybrid metaheuristic method for the Maximum Diversity Problem

Contact Info

Product

Resources

About