Greedy randomized adaptive search procedure for analog test point selection

Lei, Huajun; Qin, Kaiyu

doi:10.1007/s10470-014-0273-5

Cited by 17 publications

(4 citation statements)

References 16 publications

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“…Formula (7) indicates that if the aircraft i p arrives at the designated s D is 1, otherwise it is 0. Formula (8) indicates that if the aircraft i p is deiced by vehicle j v is 1, otherwise it is 0. Formula (9) indicates that each aircraft is serviced by k vehicles.…”

Section: The Optimization Goalmentioning

confidence: 99%

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A Heuristics Algorithm for Centralized Deicing Scheduling Problem

Shen¹,

Yang²,

Wang³

et al. 2019

Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)

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For the solution of the deicing delay, the greedy randomized adaptive search procedure (GRASP) was applied to optimize the scheduling scheme. Based on the simulation software, the departure data of an international airport was used to verify the validity of the proposed algorithm and compare the simulation results with the first-come, first-served (FCFS), greedy without availability check (GWOAC), and greedy with availability check (GWAC). In the model, the cost of the deicing time window, the deicing queue time and the moving distances of aircraft were included in the objective function. The simulation experiments on 283 instances results show that GRASP outperforms other algorithms on aircraft deicing queue time and take-off delay time. Furthermore, in order to obtain an optimal scheduling scheme, vehicles' workload is balanced by adjusting the model after evaluating the working hours of each deicing vehicle.

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Section: The Optimization Goalmentioning

confidence: 99%

“…GRASP is a multi-step iterative algorithm, each iteration contains two stages [8]. The first stage is to construct a preliminary feasible solution.…”

Section: Greedy Randomized Adaptive Search Proceduresmentioning

confidence: 99%

A Heuristics Algorithm for Centralized Deicing Scheduling Problem

Shen¹,

Yang²,

Wang³

et al. 2019

Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)

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show abstract

“…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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“…In paper [10], the test point selection procedure was transformed into a graph node expanding procedure and utilized entropy of information to guide graph search, and the method is subsequently improved by Gao et al [11]. A greedy randomized adaptive search algorithm was proposed to find the global optimal test point set [12]. A multiobjective fruit fly optimization algorithm was proposed to enhance the global test point selection ability [13].…”

Section: Introductionmentioning

confidence: 99%

Test Point Selection Method for Analog Circuit Fault Diagnosis Based on Similarity Coefficient

Meng

Zhou

et al. 2018

Mathematical Problems in Engineering

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The demand for testability analysis has increased with the integration densities and complexity of circuits. As an important part of testability analysis, the test point selection method needs to be researched in depth. A new similarity coefficient criterion is proposed to determine the fault isolation degree because output responses of a circuit with component tolerance are approximately subject to the normal distribution. Then, a new test point selection method is proposed based on the fault-pair similarity coefficient criterion information table. Simulation experiments are used to validate the accuracy of the proposed method in terms of the optimum test point set and fault isolation degree. The results show that the proposed method improves the performance of test point selection by comparing with the other reported methods.

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Greedy randomized adaptive search procedure for analog test point selection

Cited by 17 publications

References 16 publications

A Heuristics Algorithm for Centralized Deicing Scheduling Problem

A Heuristics Algorithm for Centralized Deicing Scheduling Problem

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Test Point Selection Method for Analog Circuit Fault Diagnosis Based on Similarity Coefficient

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