A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines

Sangaiah, Arun Kumar; Suraki, Mohsen Yaghoubi; Sadeghilalimi, Mehdi; Bozorgi, Seyed Mostafa; Hosseinabadi, Ali Asghar Rahmani; Wang, Jin

doi:10.3390/sym11020165

Cited by 31 publications

(18 citation statements)

References 33 publications

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“…Basu et al [42] presented a hybrid algorithm called GAACO to solve the problem of scheduling tasks in cloud computing for IoT applications. The algorithm presented at each step uses the best combination of tasks and avoids getting stuck in the local optimal.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

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The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

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Section: Related Workmentioning

confidence: 99%

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

Self Cite

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“…We carried out studies to identify better algorithms to find optimal solutions for the QAP. Some of these studies suggest that some algorithms may be improved using parallel approaches [33][34][35][36], while other studies suggest some algorithms based on the hybrid approaches [37][38][39], which have proven its superiority over other types of algorithms because it has addressed the issues found in other algorithms by using their advantages. e study [40] has been presented a hybrid algorithm between the memetic algorithm (MA) and tabu search (TS) algorithm that was used to improve the local improvement strategy.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Method Integrating a Discrete Differential Evolution Algorithm with Tabu Search Algorithm for the Quadratic Assignment Problem: A New Approach for Locating Hospital Departments

Hameed

Mutar

Alrikabi

et al. 2021

Mathematical Problems in Engineering

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The facility layout problem (FLP) is a very important class of NP-hard problems in operations research that deals with the optimal assignment of facilities to minimize transportation costs. The quadratic assignment problem (QAP) can model the FLP effectively. One of the FLPs is the hospital facility layout problem that aims to place comprehensive clinics, laboratories, and radiology units within predefined boundaries in a way that minimizes the cost of movement of patients and healthcare personnel. We are going to develop a hybrid method based on discrete differential evolution (DDE) algorithm for solving the QAP. In the existing DDE algorithms, certain issues such as premature convergence, stagnation, and exploitation mechanism have not been properly addressed. In this study, we first aim to discover the issues that make the current problem worse and to identify the best solution to the problem, and then we propose to develop a hybrid algorithm (HDDETS) by combining the DDE and tabu search (TS) algorithms to enhance the exploitation mechanism in the DDE algorithm. Then, the performance of the proposed HDDETS algorithm is evaluated by implementing on the benchmark instances from the QAPLIB website and by comparing with DDE and TS algorithms on the benchmark instances. It is found that the HDDETS algorithm has better performance than both the DDE and TS algorithms where the HDDETS has obtained 42 optimal and best-known solutions from 56 instances, while the DDE and TS algorithms have obtained 15 and 18 optimal and best-known solutions out of 56 instances, respectively. Finally, we propose to apply the proposed algorithm to find the optimal distributions of the advisory clinics inside the Azadi Hospital in Iraq that minimizes the total travel distance for patients when they move among these clinics. Our application shows that the proposed algorithm could find the best distribution of the hospital’s rooms, which are modeled as a QAP, with reduced total distance traveled by the patients.

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“…Accuracy, specificity, and sensitivity are deployed to evaluate the performances of the classifiers, such as training time and prediction accuracy. Parallel computing can be utilized to improve the efficiency [29,30].…”

Section: Classification Stagementioning

confidence: 99%

A study of hepatic fibrosis staging methods using diffraction enhanced imaging

Wang

Zhou

et al. 2020

J Image Video Proc.

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The early hepatic fibrosis staging is very important for timely diagnosis, prognosis, and treatment of all chronic liver diseases. Diffraction-enhanced imaging, which can provide much more information on soft tissue morphology than conventional absorption radiography, might be a potential noninvasive technique to diagnose and stage hepatic fibrosis. This paper presents different feature extraction strategies and classification methods to automatically classify hepatic fibrosis using diffractionenhanced imaging images. Texture features are obtained using a total of three methods including first order feature, gray level co-occurrence matrix, and grayscale gradient co-occurrence matrix. The fusion of these texture features is also studied. The principal component analysis is used to reduce the dimension of the features and redundant information among data. The features are classified using two popular classification techniques, namely, K-nearest neighbors and support vector machines. On the basis of the comparison of different feature strategies and classification methods, we can identify the suitable methods for grading hepatic fibrosis. The proposed approach efficiently classifies the hepatic fibrosis DEI images into four classes with the highest classification accuracy of 99.99%. We further demonstrate the potential of the DEI images in staging hepatic fibrosis.

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A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines

Cited by 31 publications

References 33 publications

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

A Hybrid Method Integrating a Discrete Differential Evolution Algorithm with Tabu Search Algorithm for the Quadratic Assignment Problem: A New Approach for Locating Hospital Departments

A study of hepatic fibrosis staging methods using diffraction enhanced imaging

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