Greedy Randomized Adaptive Search Procedure with Path-Relinking for the Vertex p-Center Problem

Yin, Aijun; Zhou, Taoqing; Ding, Junwen; Zhao, Qingjie; Lv, Zhipeng

doi:10.1007/s11390-017-1802-3

Cited by 9 publications

(19 citation statements)

References 43 publications

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“…In this paper, a greedy randomized adaptive search procedure [18,19,20] is used to expand the bicluster seeds, which includes the construction phase [21,22] elements and greedy function values should be updated promptly to reflect their changes, which is the embodiment of the self-adaption function in the algorithm. The greedy function [7,8] in this paper is:…”

Section: Expansion Of Bicluster Seeds Based On Greedy Randomized Adapmentioning

confidence: 99%

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Yang¹,

Zhao²,

Shu³

et al. 2018

JMI

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The biclustering algorithm based on greedy randomized adaptive search procedure (GRASP) has two main steps, firstly, bicluster seeds are generated by Kmeans clustering; secondly, the greedy randomized adaptive search procedure is used to expand these bicluster seeds so that to get some better biclusters. However, K-means clustering assumes that each gene belongs to only one cluster, which is not biologically valid, because some genes may not belong to only one category, and K-means clustering requires multiple runs to determine the number of "clusters," all of which will affect the identification of bicluster seeds. This paper optimizes GRASP-based bIclustering algorithm. In this paper, the single column vector clustering is used to generate biclusters seed, and then the bicluster seeds are added more rows and columns to generate the final biclusters. Experiment shows that this algorithm can generate a large number of biclusters with coexpression level, and the algorithm also finds more biclusters.

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Section: Expansion Of Bicluster Seeds Based On Greedy Randomized Adapmentioning

confidence: 99%

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Yang¹,

Zhao²,

Shu³

et al. 2018

JMI

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“…Ferone et al (2017) proposed a Greedy Randomized Adaptive Search (GRASP) for the problem. Yin et al (2017) used the local search devised in Pullan (2008) with a modified tabu strategy within a GRASP metaheuristic with Path Relinking (PR). Two more heuristics for the problem were evaluated by Yadav and Prakash (2020).…”

Section: Introductionmentioning

confidence: 99%

“…To the best of the author's knowledge, the current state-of-the-art metaheuristics for pC are those proposed by Pullan (2008) and Yin et al (2017). No metaheuristic has yet been proposed for αNpC, to the best of the author's knowledge, and the current state-of-the-art metaheuristic for pNC is the hybrid metaheuristic proposed by López-Sánchez et al (2019).…”

Section: Introductionmentioning

confidence: 99%

“…The main contributions of this paper are new state-of-the-art metaheuristics for the pC, αNpC, and pNC problems. More specifically, metaheuristics are proposed to outperform the state-of-the-art metaheuristics of Yin et al (2017) and López-Sánchez et al (2019) for pC and pNC, respectively, and another metaheuristic as the (first) stateof-the-art metaheuristic for αNpC.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting flat subspaces in local search for p-Center problem and two fault-tolerant variants

Mousavi

2023

Computers & Operations Research

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“…It can get high quality solutions for the classical benchmarks. Yin et al [14] proposed a greedy randomized adaptive search procedure with path-relinking (GRASP/PR) algorithm, which combines both GRASP and path-relinking. In their algorithm, each iteration of GRASP/PR consists of the construction of a randomized greedy solution, followed by a tabu search procedure.…”

Section: Introductionmentioning

confidence: 99%

Effective Approaches to Solve P-Center Problem via Set Covering and SAT

Liu¹,

Fang²,

Chen³

et al. 2020

IEEE Access

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The classic p-center problem consists of choosing a set of p vertices in an undirected graph as facilities in order to minimize the maximum distance between each client vertex and its closest facility. The problem is equivalent to covering all vertices by no more than p circles with the smallest possible radius, which can be tackled by solving a series of the decision version of set covering subproblems with the same cardinality constraint (≤ p) and gradually decreasing the covering radius. In this paper, we solve the p-center problem via set covering and SAT. We first transform the p-center problem into a series of set covering subproblems and simplify them by some reduction rules. Then, we present two kinds of encoding methods to convert them into CNF format and solve them with several state-of-the-art SAT solvers. Tested on three sets of totally 70 benchmark instances, our proposed approach can improve the previous best known results for 3 instances using the heuristic SAT solvers while proving the optimality for 59 instances using the exact SAT solvers. The computational results demonstrate the effectiveness of the proposed approach in terms of both solution quality and computational efficiency. In addition, the main advantage of our approach is twofold: The independence of the subproblems allows the problem to be solved in parallel; The approach to transform the original problem into SAT is flexible such that various state-of-the-art SAT solvers can be used.

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Greedy Randomized Adaptive Search Procedure with Path-Relinking for the Vertex p-Center Problem

Cited by 9 publications

References 43 publications

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Exploiting flat subspaces in local search for p-Center problem and two fault-tolerant variants

Effective Approaches to Solve P-Center Problem via Set Covering and SAT

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