Fuzzy-based mapping algorithms to design networks-on-chip

Taassori, Mehdi; Niroomand, Sadegh; Uysal, Sener; Hadi-Vencheh, A.; Vízvári, Béla

doi:10.3233/ifs-162105

Cited by 16 publications

(4 citation statements)

References 37 publications

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“…Moreover, researchers have also adopted meta-heuristic algorithms to aid different computing techniques to solve application mapping problems for NoC-based systems. Such as, genetic algorithm (GA) and simulated annealing (SA) are utilized to map the tasks to the cores of NoC in [16], which is then optimized for power consumption and performance using fuzzy rules.…”

Section: Introductionmentioning

confidence: 99%

Application Mapping Using Cuckoo Search Optimization With Lévy Flight for NoC-Based System

et al. 2021

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Network on chip (NoC) is a promising communication infrastructure for multiple cores on a chip to exchange data efficiently. In such NoC architecture, application mapping is the process of assigning tasks to the processing cores. An optimized application mapping technique enhances the performance of a chip and reduces the entire chip's energy consumption. The optimization of application mapping is essential in the design of NoC. In this study, the greedy algorithm is utilized as the first technique to place the maximum communicating tasks together to give the main algorithm a head start. Then, a meta-heuristic Cuckoo Search via Lévy flight is employed further to optimize the placement of tasks on the NoC cores. The greedy algorithm furnishes a relatively pre-processed base to the CSO, which eventually helps in the fast convergence of the main algorithm. The analysis of the results shows that the proposed algorithm outperformed the state-of-the-art techniques in NoC application mapping in terms of various performance metrics, such as communication cost, energy consumption, and average packet latency.

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Section: Introductionmentioning

confidence: 99%

Application Mapping Using Cuckoo Search Optimization With Lévy Flight for NoC-Based System

et al. 2021

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“…Optimization problems particularly the supply chain network design problem can be even more interesting and realistic when are tackled in uncertain environments considering uncertain values for parameters and even variables (see Boros, Fehér, Lakner, Niroomand, & Vizvári, 2016; Mahmoodirad, Allahviranloo, & Niroomand, 2019; Mahmoodirad, Dehghan, & Niroomand, 2019; Malaperdas & Zacharias, 2019; Mirzaei, Niroomand, & Zare, 2016; Niroomand, Bazyar, Alborzi, & Mahmoodirad, 2018; Niroomand, Hadi‐Vencheh, Mirzaei, & Molla‐Alizadeh‐Zavardehi, 2016; Parlak et al, 2019; Sanei, Mahmoodirad, & Niroomand, 2016, Taassori, Niroomand, Uysal, Hadi‐Vencheh, & Vizvari, (2016)). For coping with such problems with uncertain parameters, in existence of the historical information of the uncertain parameters, the approaches like fuzzy theory, stochastic programming, probability theory can be applied.…”

Section: Introductionmentioning

confidence: 99%

Uncertain location–allocation decisions for a bi‐objective two‐stage supply chain network design problem with environmental impacts

Mahmoodirad

Niroomand²

2020

Expert Systems

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In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chanceconstrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.

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“…Therefore, a multiobjective modelling and optimization (Boros, Fehér, Lakner, Niroomand, & Vizvári, ; Cardillo, Cascini, Frillici, & Rotini, ; Franco, Jablonsky, Leopold‐Wildburger, & Montibeller, ; Ghasemi & Varaee, ; Gholizadeh & Baghchevan, ; Gutiérrez, Huerga, & Novo, ; Jablonsky, ; Jiménez, Bilbao‐Terol, & Arenas‐Parra, ; Kovács & Marian, ; Özmen, Karakaya, & Köksalan, ; Sanei, Mahmoodirad, Niroomand, Jamalian, & Gelareh, ; Taassori et al, ) is a suitable approach for incorporating the above‐mentioned objectives. Furthermore, when some parameters of the problem have uncertain nature, it must be reflected in the model as well (Fullér, Canós‐Darós, & Canós‐Darós, ; Fullér & Majlender, ; Moloudzadeh, Allahviranloo, & Darabi, ; Niroomand, Mahmoodirad, Heydari, Kardani, & Hadi‐Vencheh, ; Salahshour & Allahviranloo, ; Salmasnia, Khatami, Baradaran Kazemzadeh, & Zegordi, ; Taassori, Niroomand, Uysal, Hadi‐Vencheh, & Vizvari, ; Wang, Zhou, Li, Zhang, & Chen, ). In this study, the demand values, raw material price, and the discounts' break points for each size of sheet offered by the suppliers have uncertain nature.…”

Section: Introductionmentioning

confidence: 99%

A hybrid solution approach for fuzzy multiobjective dual supplier and material selection problem of carton box production systems

Niroomand¹,

Mahmoodirad

Mosallaeipour

2018

Expert Systems

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A novel optimization problem of carton box manufacturing industries is introduced in this paper. A mixed integer linear formulation with multiple objective functions is developed in order to determine the value of some criteria of carton raw sheets such as size, amount, and supplier under simultaneous minimization of multiple goals such as purchasing cost of raw sheets under discount policy, wastage remained from raw sheets, and quantity of surplus of carton boxes. In order to cope with the unstable market of this sector, some parameters of the proposed formulation such as demand value of the products and price given for raw sheets are assumed to be fuzzy numbers. To tackle such fuzzy multiobjective problem, first, the fuzzy problem is converted to a crisp form using the concepts of necessity‐based chance‐constrained modelling approach. Then a new hybrid form of the fuzzy programming approach is proposed to solve the obtained crisp multiobjective problem effectively. Computational experiments on a real case given by a carton box factory show the superior result of the proposed solution approach compared with the well‐known multiobjective solution methods taken from the literature.

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Fuzzy-based mapping algorithms to design networks-on-chip

Cited by 16 publications

References 37 publications

Application Mapping Using Cuckoo Search Optimization With Lévy Flight for NoC-Based System

Application Mapping Using Cuckoo Search Optimization With Lévy Flight for NoC-Based System

Uncertain location–allocation decisions for a bi‐objective two‐stage supply chain network design problem with environmental impacts

A hybrid solution approach for fuzzy multiobjective dual supplier and material selection problem of carton box production systems

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