A bi-objective hub maximal covering location problem considering time-dependent reliability and the second type of coverage

Pasandideh, Seyed Hamid Reza; Niaki, Seyed Taghi Akhavan; Sheikhi, Mehri

doi:10.1080/17509653.2015.1056265

Cited by 10 publications

(9 citation statements)

References 27 publications

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“…The parameter is the index of image in database which is a rounded situation of i-th grasshopper. (7) In which, [. ] indicates integer part and j is the place of grasshopper in the GOA.…”

Section: Modi2ied Goamentioning

confidence: 99%

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A Modified Grasshopper Optimization Algorithm Combined with Convolutional Neural Network for Content Based Image Retrieval

Sezavar

Farsi

Mohamadzadeh

2019

IJE

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A B S T R A C TNowadays, with huge progress in digital imaging, new image processing methods are needed to manage digital images stored on disks. Image retrieval has been one of the most challengeable fields in digital image processing which means searching in a big database in order to represent similar images to the query image. Although many efficient researches have been performed for this topic so far, there is a semantic gap between human concept and features extracted from the images and it has become an important problem which decreases retrieval precision. In this paper, a convolutional neural network (CNN) is used to extract deep and high-level features from the images. Next, an optimization problem is defined in order to model the retrieval system. Heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have shown an effective role in solving the complex problems. A recent introduced heuristic algorithm is Grasshopper Optimization Algorithm (GOA) which has been proved to be able to solve difficult optimization problems. So, a new search method, modified grasshopper optimization algorithm (MGOA) is proposed to solve modeled problem and to retrieve similar images efficiently, despite of total search in database. Experimental results showed that the proposed system named CNN-MGOA achieves superior accuracy compared to traditional methods.

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“…The parameter is the index of image in database which is a rounded situation of i-th grasshopper. (7) In which, [. ] indicates integer part and j is the place of grasshopper in the GOA.…”

Section: Modi2ied Goamentioning

confidence: 99%

“…Feature extraction has been an important task in signal processing areas, image, video and speech processing [6][7]. Also, the main part of any image retrieval system is feature extraction.…”

Section: Introductionmentioning

confidence: 99%

A Modified Grasshopper Optimization Algorithm Combined with Convolutional Neural Network for Content Based Image Retrieval

Sezavar

Farsi

Mohamadzadeh

2019

IJE

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“…Constraints set (5) state that in each period, node i can be allocated to node k, if node k is served as a hub. Constraints set (6) show that in each period, the path i → k → l → j will be established where node i is allocated to hub k and node j is allocated to hub l. Constraints set (7) ensure that in each period, the path i → k → l → j is established, where traveling time is less than the coverage radius of β. Constraints set (8) and (9) indicate the possibility of creating and closing hubs over different periods.…”

Section: Notations and Parametersmentioning

confidence: 99%

“…Also, the modified non-dominated sorting genetic algorithm II (NSGAII) was used to solve the problem. Pasandideh et al [7] presented a bi-objective model for uncapacitated single allocation MHCP considering time-dependent reliabilities. They used the second type of coverage, and the objectives of the problem including maximizing the flow and the reliability of the network.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Model for Bi-objective Maximal Hub Covering Problem with Periodic Variations of Parameters

Khosravian

Nookabadi

Moslehi

2019

IJE

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A B S T R A C TT he problem of maximal hub covering as a challenging problem in operation research. Transportation programming seeks to find an optimal location of a set of hubs to reach maximum flow in a network. Since the main structure's parameters of the problem such as origin-destination flows, costs and travel time, change periodically in the real world applications, new issues arise in handling it. In this paper, to deal with the periodic variations of parameters, a bi-objective mathematical model is proposed for the single allocation multi-period maximal hub covering problem. T he ε-constraint approach has been applied to achieve non-dominated solutions. Given that the single-objective problem found in the εconstraint method is computationally intractable. Benders decomposition algorithm by adding valid inequalities is developed to accelerate the solution process. Finally, the proposed method is carried out by CAB data set, and the results confirm the efficiency of it regarding optimality and running time.

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“…Jabalameli et al [12] proposed an efficient simulated annealing based heuristic for solving the single allocation p-hub maximal covering problem. Pasandideh et al [22] proposed a bi-objective hub maximal covering problem considering time-dependent reliabilities. Karimi et al [14] formulated the multi-modal single allocation capacitated p-hub covering problem over fully interconnected hub networks.…”

Section: Literature Reviewmentioning

confidence: 99%

A Tabu Search Based Solution Approach to the Competitive Multiple Allocation Hub Location Problem

Ghaffarinasab

Jabarzadeh

Motallebzadeh

2017

IJOR

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A bi-objective hub maximal covering location problem considering time-dependent reliability and the second type of coverage

Cited by 10 publications

References 27 publications

A Modified Grasshopper Optimization Algorithm Combined with Convolutional Neural Network for Content Based Image Retrieval

A Modified Grasshopper Optimization Algorithm Combined with Convolutional Neural Network for Content Based Image Retrieval

Mathematical Model for Bi-objective Maximal Hub Covering Problem with Periodic Variations of Parameters

A Tabu Search Based Solution Approach to the Competitive Multiple Allocation Hub Location Problem

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