A Refined Evaluation Function for the MinLA Problem

Rodríguez-Tello, Eduardo; Hao, Jin‐Kao; Torres-Jiménez, José

doi:10.1007/11925231_37

Cited by 5 publications

(5 citation statements)

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“…By extending a preliminary work reported in [13], this paper described an in-depth investigation of the notion of evaluation function using a well-known graph labeling problem (i.e., the Minimum Linear Arrangement problem, MinLA) as a representative case of study.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…This section presents a detailed analysis of certain characteristics of LA and highlights the potential drawbacks of LA when it is directly used as an evaluation function. This analysis has led to useful insight and information which guided us to design a more discriminating evaluation function introduced in [13].…”

Section: Analyzing the Evaluation Functionmentioning

confidence: 99%

“…Section 2 formally introduces the MinLA problem, analyzes some characteristics of the basic evaluation function LA and shows how these results were used in [13] to propose the more informative Φ evaluation function. Section 3 shows a first assessment of the usefulness of Φ with respect to the conventional LA function, with the help of a parameter-free Steepest Descent (SD) algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is a need for heuristics to address this problem in reasonable time. Among the reported algorithms are: a) heuristics especially developed for MinLA, such as the Binary Decomposition Tree heuristic [9], the Multi-Scale algorithm [10] and the Algebraic Multi-Grid scheme [23]; b) metaheuristics such as Simulated Annealing [8,12] and Memetic Algorithms [11,13]; and c) some combinations of these methods.…”

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confidence: 99%

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Boosting the performance of metaheuristics for the MinLA problem using a more discriminating evaluation function

Rodríguez-Tello

2015

Teh. vjesn.

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Original scientific paper This paper investigates the role of evaluation function used by metaheuristics for solving combinatorial optimization problems. Evaluation function (EF) is a key component of any metaheuristic algorithm and its design directly influences the performance of such an algorithm. However, the design of more discriminating EFs is somewhat overlooked in the literature. We present in this work the first in-depth analysis of the conventional EF for the Minimum Linear Arrangement (MinLA) problem. The results from this study highlighted its potential drawbacks and led to useful insight and information which guided us to design a new more discerning EF. Its practical usefulness was assessed within three different algorithms: a parameter-free Steepest Descent, an Iterated Local Search and a Tabu Search. The analysis of the data produced by these comparisons showed that the performance of the three adopted approaches could be boosted by using the proposed more discriminating EF. Keywords: combinatorial optimization, evaluation function, linear arrangement problem, metaheuristics Poboljšanje učinaka metaheuristike kod MinLA problema primjenom kritičnije funkcije evaluacijeOriginal scientific paper U radu se ispituje uloga funkcije evaluacije u metaheuristici kod rješavanja kombinatornih problema optimizacije. Evaluacijska funkcija (EF) je ključna sastavnica svakog metaheurističkog algoritma i njezin dizajn direktno utječe na performansu takvog algoritma. Međutim, u literaturi je dizajn kritičnijih EF-a donekle zanemaren. U ovom radu dajemo prvu temeljnu analizu standardne EF za problem Minimum Linear Arrangement (MinLA). Dobiveni rezultati su ukazali na moguće nedostatke i dali koristan uvid i informacije potrebne za dizajniranje kritičnije EF. Njezina se praktična korisnost procijenila u tri različita algoritma: parameter-free Steepest Descent, Iterated Local Search i Tabu Search. Analiza dobivenih podataka pokazala je da bi se performansa ta tri primijenjena pristupa mogla poboljšati primjenom predloženih kritičnijih EF.Ključne riječi: kombinatorna optimizacija, funkcija evaluacije, problem linearnog uređenja, metaheuristika

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Analyzing the Evaluation Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Boosting the performance of metaheuristics for the MinLA problem using a more discriminating evaluation function

Rodríguez-Tello

2015

Teh. vjesn.

Self Cite

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“…Simulated Annealing is the most traditional approach to optimize data sets, which leads to good results as it successfully escapes local minima (Johnson et al, 1989). However, this method features nonlinear runtime complexity and therefore cannot be applied on large-scale data sets (Rodriguez-Tello et al, 2006;Rodriguez-Tello et al, 2008). Consequently, faster concepts using multi-grid techniques (Brandt et al, 1986) became more popular.…”

Section: Related Workmentioning

confidence: 99%

Optimizing large knowledge networks in spatial computers

Pacher

Binna

Specht

2016

The Knowledge Engineering Review

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This paper presents a novel concept of a Spatially Aware Graph Store, which realizes a Graph Store on top of a spatial computer architecture to manage graphs in one, two or three physical dimensions. In this environment, the physical distance between graph nodes strongly affects graph traversal performance. Consequently, a Spatially Aware Graph Store needs to minimize these distances to operate efficiently. We show that this minimization can be achieved in two ways. First, by increasing the dimensionality of the spatial computer and second by applying optimization methods. For the latter, this work introduces a novel Mid Point Optimization method to quickly optimize large real-world knowledge networks by rearranging nodes in a way that distances between linked nodes are reduced. In addition, a Local Optimization method is subsequently applied to refine the result. Finally, the Node Decomposition method is presented that splits nodes with many edges into several smaller nodes to achieve a further reduction of distances between linked nodes.Our results show that the overall distances between nodes can be reduced by three orders of magnitude for 3D in comparison to one-dimensional (1D) Spatially Aware Graph Stores. The suggested Mid Point Optimization method achieves a reduction by another order of magnitude. In a 3D spatial computer, Local Optimization is capable of reducing distances by another 20%. However, in 1D and 2D spatial computers it becomes a prohibitive time consuming method. Finally, the Node Decomposition enables an additional distance reduction by 40% in Scale Free Graph Data sets.

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JAM: A Tabu-Based Two-Stage Simulated Annealing Algorithm for the Multidimensional Arrangement Problem

Aroca

Anta

2014

Hybrid Metaheuristics

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A Refined Evaluation Function for the MinLA Problem

Cited by 5 publications

References 11 publications

Boosting the performance of metaheuristics for the MinLA problem using a more discriminating evaluation function

Boosting the performance of metaheuristics for the MinLA problem using a more discriminating evaluation function

Optimizing large knowledge networks in spatial computers

JAM: A Tabu-Based Two-Stage Simulated Annealing Algorithm for the Multidimensional Arrangement Problem

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