Evolutionary algorithms for overlapping correlation clustering

Andrade, Carlos Eduardo de; Resende, Maurício G. C.; Karloff, Howard; Miyazawa, Flávio Keidi

doi:10.1145/2576768.2598284

Cited by 14 publications

(6 citation statements)

References 10 publications

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“…To search for good solutions for the MMpLHP, we implemented a biased random‐key genetic algorithm (BRKGA) . Our choice was mainly grounded on recent successes with classical hard combinatorial optimization problems such as routing covering , packing scheduling , and clustering .…”

Section: Introductionmentioning

confidence: 99%

Heuristics for a hub location‐routing problem

Lopes

Andrade²,

Queiroz

et al. 2016

Networks

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We investigate a variant of the many‐to‐many hub location‐routing problem which consists in partitioning the set of nodes of a graph into routes containing exactly one hub each, and determining an extra route interconnecting all hubs. A variable neighborhood descent with neighborhood structures based on remove/add, swap and exchange moves nested with routing and location operations is used as a local search procedure in a multistart algorithm. We also consider a sequential version of this local search in the multistart. In addition, a biased random‐key genetic algorithm working with a local search routine, which also considers routing and location operations, is applied to the problem. To compare the heuristic solutions, we develop an integer programming formulation which is solved with a branch‐and‐cut algorithm. Capacity and path elimination constraints are added in a cutting plane fashion. The separation algorithms are based on the computation of min‐cut trees and on the connected components of a support graph. Computational experiments were conducted on several benchmark instances of routing problems and show that the heuristics are effective on medium to large‐sized instances, while the branch‐and‐cut algorithm solves small to medium sized problems to optimality. These algorithms were also compared with a commercial hybrid solver showing that the heuristics are quite competitive. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 68(1), 54–90 2016

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Section: Introductionmentioning

confidence: 99%

Heuristics for a hub location‐routing problem

Lopes

Andrade²,

Queiroz

et al. 2016

Networks

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“…The index is the index of the core of the new group. If the similarity between a rule and the core is positive and under the given memory limit, the rule is added to the vector H [1]; otherwise, it is added to the vector H [2] [2] will be processed in function assignCluster until empty. After we obtain the grouping result O, we can build the trie data structure, as shown in Figure 2.…”

Section: Genmatcher Grouping Algorithmmentioning

confidence: 99%

“…This heuristic often leaves some memory on the table but is much faster (at configuration time) than iteratively grouping and expanding to hit a given memory target. Figure 11 shows the memory cost per rule with respect to Bitweaving and GenMatcher for all the six different rule sizes (256,512,1,024,2,048,4,096,8,192). For all cases except 8,192, GenMatcher has a lower memory cost per rule than Bitweaving.…”

Section: S#mentioning

confidence: 99%

“…Thus, more generic packet matching methods are required to deal with all the various matching requirements. 2 Prefix matching has been most heavily studied in prior work [40]. An important improvement for software-based prefix matching is the trie data structure [40].…”

Section: Introductionmentioning

confidence: 99%

“…Challenges: GenMatcher employs its grouping algorithm to allocate all the rules into minimal groups under a given memory constraint threshold. In general, clustering [2,3,5,9,28,35,44,45] is a powerful tool for finding the underlying structure of large datasets [19,30]. Our goal is to apply the clustering result to build a data structure for efficient arbitrary matching.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GenMatcher

Wang

McHale

Gratz

et al. 2018

ACM Trans. Archit. Code Optim.

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Packet classification methods rely upon packet content/header matching against rules. Thus, throughput of matching operations is critical in many networking applications. Further, with the advent of Software Defined Networking (SDN), efficient implementation of software approaches to matching are critical for the overall system performance. This article presents 1 GenMatcher, a generic, software-only, arbitrary matching framework for fast, efficient searches. The key idea of our approach is to represent arbitrary rules with efficient prefix-based tries. To support arbitrary wildcards, we rearrange bits within the rules such that wildcards accumulate to one side of the bitstring. Since many non-contiguous wildcards often remain, we use multiple prefix-based tries. The main challenge in this context is to generate efficient trie groupings and expansions to support all arbitrary rules. Finding an optimal mix of grouping and expansion is an NP-complete problem. Our contribution includes a novel, clustering-based grouping algorithm to group rules based upon their bit-level similarities. Our algorithm generates near-optimal trie groupings with low configuration times and provides significantly higher match throughput compared to prior techniques. Experiments with synthetic traffic show that our method can achieve a 58.9X speedup compared to the baseline on a single core processor under a given memory constraint.

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Integer Programming Based Algorithms for Overlapping Correlation Clustering

Mashiach,

Sharan

2024

Lecture Notes in Computer Science

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Evolutionary algorithms for overlapping correlation clustering

Cited by 14 publications

References 10 publications

Heuristics for a hub location‐routing problem

Heuristics for a hub location‐routing problem

GenMatcher

Integer Programming Based Algorithms for Overlapping Correlation Clustering

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