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Optimal replacement strategy of obsolete industrial components under fuzzy data

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2020年第3期234卷 349-357页

作者： Mellal, Mohamed Arezki Williams, Edward J. MHamed Bougara Univ Fac Engn Sci FSI LMSS Boumerdes 35000 Algeria Univ Michigan Coll Engn & Comp Sci Ind & Mfg Syst Engn Dept Dearborn MI 48128 USA Univ Michigan Coll Business Decis Sci Dearborn MI 48128 USA

Competitiveness and rapid technological advances lead to the obsolescence of household items and industrial components. The challenge to people and industrial firms is to be equipped with the latest items, but at the lowest cost. During the last decade, several works have investigated various replacement strategies in order to optimally replace the obsolete industrial components. This article introduces the first work of the literature addressing a replacement strategy in case of fuzzy data on the components. The approach is based on fuzzy logic and the cuckoo optimization algorithm. Two case studies are illustrated for highlighting the applicability of the approach proposed.

关键词： Obsolescence replacement strategy fuzzy logic cuckoo optimization algorithm

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Length prediction of silicon nanowires (SiNWs) prepared by the MACE method using the ANN-COA-PSO algorithm for high supercapacitor applications

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JOURNAL OF PHYSICS AND CHEMISTRY OF SOLIDS 2021年 156卷 110146-110146页

作者： Zeraati, Malihe Chen, Tzu-Chia Ebri, Masoumeh Chauhan, Narendra Pal Singh Sargazi, Ghasem Shahid Bahonar Univ Kerman Dept Mat Engn Kerman 761694111 Iran Krirk Univ Int Coll Business Adm Program 3 Soi Ramindra 1 Bangkok 10220 Thailand Islamic Azad Univ Dept Comp Syst Architecture Branch Electron Tehran Iran Bhupal Nobles Univ Fac Sci Dept Chem Udaipur 313002 Rajasthan India Bam Univ Med Sci Noncommunicable Dis Res Ctr Bam Iran

In this study, the metal-assisted chemical etching of silicon with the help of metal is used to fabricate large-scale silicon nanowire arrays (SiNWs). This method was optimised by artificial neural networks (ANN) and the cuckoo optimization algorithm (COA) was used to optimize ANN. Finally, the particle swarm optimization (PSO) method has been used to achieve the maximum length of silicon nanowires. As a result, the nanowires produced by the 1 M NaOH electrolyte had the properties of a capacitive cloud with a capacity of 758 mF/cm(2). It has the potential to be an excellent candidate for supercapacitor applications.

关键词： SiNWs Metal-assisted chemical etching Artificial neural networks cuckoo optimization algorithm Particle swarm optimization Supercapacitor

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SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs

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COMPUTER NETWORKS 2021年 185卷 107727-107727页

作者： Tahmasebi, Shirin Rasouli, Nayereh Kashefi, Amir Hosein Rezabeyk, Elmira Faragardi, Hamid Reza Sharif Univ Technol Tehran Iran Islamic Azad Univ Hashtgerd Branch Hashtgerd Iran Islamic Azad Univ South Tehran Branch Tehran Iran Arnirkabir Univ Technol Tehran Iran KTH Royal Inst Technol Stockholm Sweden

Due to the highly dynamic nature of Wireless Sensor Networks (WSN), Software-Defined Network (SDN) is a promising technology to ease network management by providing a logically centralized control plane. It is a common approach to employ multiple SDN controllers to form a physically distributed SDN to achieve better fault tolerance, boost scalability, and enhance performance. Despite physical distribution, since the notion behind SDN is to logically centralize network management, it is essential to provide a consistent view of the network's state for all controllers. Deploying multiple controllers result in higher synchronization and deployment cost. Since network performance and inter-controller synchronization cost seem to be contradicting objectives, it is a research challenge to choose the best placement of SDN controllers to optimize both the performance and synchronization cost of an SDN-enabled WSN simultaneously. In this paper, we first formulate the controller placement problem as a multi-objective optimization problem. In this regard, multiple constraints are considered, including reliability, fault tolerance, performance in terms of latency, synchronization overhead, and deployment cost. Moreover, we leverage the cuckoo optimization algorithm, a nature-inspired population-based meta-heuristic algorithm to solve the optimization problem. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. Finally, to evaluate our proposed method, we compare it against several existing methods in the literature. The experiments reveal that our proposed method considerably outperforms existing methods, namely Simulated Annealing (SA) and Quantum Annealing (QA), in terms of both performance and synchronization cost. Additionally, our proposed algorithm, in contrast to Integer Linear Programming (ILP), is considerably more scalable, which makes it applicable for large-scale WSNs.

关键词： Wireless sensor networks Software defined networks Controller node placement cuckoo optimization algorithm Synchronization cost

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Enhanced competitive adsorption of zinc and manganese by alginate-iron oxide-combusted coal gangue composite: Synthesizing, characterization and investigation

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2021年第1期9卷

作者： Mohammadi, R. Azadmehr, A. Maghsoudi, A. Amirkabir Univ Technol Dept Min & Met Engn 424 Hafez Ave Tehran 18754413 Iran

Reusing different mine gangue is an effective method to reduce environmental hazards around the processing plants. In this study, coal gangue (CG) of Anjirtangeh coal washing plant with high levels of Loss on ignition (LOI) are first combusted, then modified by iron oxide and alginate, and used as the adsorbent of zinc and manganese. The structure of the fabricated adsorbent was identified through XRD, XRF, FTIR, SEM and BET analyses. Among these analyses, BET shows that the surface area (3 m(2)/g) and the pores diameter (32 nm) of the adsorbent are not high. Hence, the functional groups play an influential role in the adsorption capacity. Further, the cuckoo optimization algorithm was used to investigate the optimal conditions of the adsorption process and the results were investigated. The adsorption capacity of zinc and manganese in the optimal conditions was 115.6 and 101.8 mg/g, respectively, which reduced to 105 and 92.4 mg/g in competitive conditions, respectively. Isotherm studies of adsorption by different models showed that adsorption of zinc and manganese onto the alginate-iron oxide-combusted coal gangue (AICCG) composite was done chemically and desirably. Besides, the adsorption of zinc and manganese onto the fabricated composite was carried out in a heterogeneous process. Kinetic studies conducted by the use of reaction and diffusion-based models showed that the intra-particle diffusion model controls the adsorption process for both ions. Thermodynamic studies showed that the adsorption process of zinc and manganese onto the AICCG composite was spontaneous and endothermic.

关键词： Combusted coal gangue Alginate Iron oxide Zinc and manganese cuckoo optimization algorithm Isotherm

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An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms

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APPLIED SOFT COMPUTING 2018年 67卷 172-182页

作者： Bouyer, Asgarali Hatamlou, Abdolreza Azarbaijan Shahid Madani Univ Fac Comp Engn & Informat Technol Tabriz Iran Islamic Azad Univ Dept Comp Sci Khoy Branch Khoy Iran

Partitional data clustering with K-means algorithm is the dividing of objects into smaller and disjoint groups that has the most similarity with objects in a group and most dissimilarity from the objects of other groups. Several techniques have been proposed to avoid the major limitations of K-Means such as sensitive to initialization and easily convergence to local optima. An alternative to solve the drawback of the sensitive to centroids' initialization in K-Means is the K-Harmonic Means (KHM) clustering algorithm. However, KHM is sensitive to the noise and easily runs into local optima. Over the past decade, many algorithms are developed for solving this problems based on evolutionary method. However, each algorithm has its own advantages, limitations and shortcomings. In this paper, we combined K-Harmonic Means (KHM) clustering algorithm with an improved cuckoo Search (ICS) and particle swarm optimization (PSO). ICS is intended to global optimum solution using Levy flight method through changing radius in a dynamic and shrewd manner. Therefore, it is faster than standard cuckoo search. ICS is effected with PSO to avoid falling into local optima. The proposed algorithm, called ICMPKHM, solves the local optima problem of KHM with significant improvement on efficacy and stability. Experiments with benchmark datasets show that the proposed algorithm is quite insensitive to the centroids' initialization. Comparative studies with other algorithms reveal that the proposed algorithm produce high quality and stable clustering results. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Data clustering Particle swarm optimization K-harmonic means cuckoo optimization algorithm

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A Novel Smart Energy Management System in Pure Electric Motorcycle Using COA

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2019年第4期4卷 600-608页

作者： Farzaneh, Alireaz Farjah, Ebrahim Shiraz Univ Sch Elect & Comp Engn Zand Ave Shiraz *** Iran

Todays, Greenhouse Gases (GHG) emissions, densely populated urban areas air quality problems, and depleting fossil fuel concerns motivated motorcycle producers in crowded countries to explore electric propulsion systems. Due to energy storage system constraints, Pure Electric Motorcycles (PEM) range is restricted, so an effective EnergyManagement System is required to optimizePEMconsumed energy. As road curvature is an inevitable part of real roads, this article, proposes a novel Smart Energy Management System (SEMS) based on road geometry which first analyses road data to identify curvature and straight parts. Then, SEMS solves optimization problems separately and optimal speed is calculated in direct and curvature parts independently. The proposed SEMS can communicate to the driver as an eco-driving system by receiving trip time importance and presenting each road point optimal speed. Because of fast response necessity, cuckoo optimization algorithm (COA) is used in this article to solve SEMS optimization problem. For evaluation purposes, three scenarios are defined: The test road is travelled at motorcycle maximum allowable speed in the first scenario. In the 2nd one, PEM speed is also constant but it equals to direct road optimal speed instead of max. speed. Independent optimal speed in direct and curvature parts is considered in 3rd scenario. Simulation results show the significant effect of road curves on consumed energy and SEMS good performance in optimization of PEM consumed energy.

关键词： Energy Management System Electric Motorcycle Energy Consumption optimization Road Curve Range cuckoo optimization algorithm

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Comparison of LLNF, ANN, and COA-ANN Techniques in Modeling the Uniaxial Compressive Strength and Static Young's Modulus of Limestone of the Dalan Formation

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NATURAL RESOURCES RESEARCH 2019年第1期28卷 223-239页

作者： Mokhtari, Maryam Behnia, Mahmoud Yazd Univ Dept Civil Engn Fac Engn POB 89195-741 Yazd Iran Isfahan Univ Technol Dept Min Engn Esfahan *** Iran

The uniaxial compressive strength and static Young's modulus are among the key design parameters typically used in geotechnical engineering projects. In this paper, three artificial intelligence techniques, namely the local linear neuro-fuzzy (LLNF) technique, artificial neural network (ANN) and the hybrid cuckoo optimization algorithm-artificial neural network (COA-ANN), were used to estimate the uniaxial compressive strength and the static Young's modulus of limestone. For this purpose, 115 limestone samples were subjected to the tests of uniaxial compressive strength, ultrasonic velocity, and physical properties (density and porosity) tests. From the laboratory results obtained, the values of the P-wave velocity, density, porosity and dynamic Poisson's ratio were tested as the model input parameters to determine the best input configuration for estimating the uniaxial compressive strength and the static Young's modulus. Different models with different input combinations were practiced, and the models with the highest estimation accuracy are reported here. Performance evaluation was carried out using three criteria including coefficient of determination, variance accounted for, and normalized mean-square error. Evaluating the correlation coefficients and error criteria resulting from the three methods used demonstrates the superiority of LLNF method to ANN and COA-ANN methods. The developed ANN models display lower correlation coefficients and higher amount of error compared to the other models. However, using cuckoo optimization algorithm has led to significant improvement in accuracy and precision of estimations carried out by ANN and has improved its efficiency. Results have confirmed that the employed hybrid method outperforms in estimating untrained data (test data) compared to the LLNF and ANN methods.

关键词： Uniaxial compressive strength Static Young's modulus Ultrasonic velocity Local linear neuro-fuzzy Artificial neural network cuckoo optimization algorithm

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cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs

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SENSORS 2020年第11期20卷 3231.-3231.页

作者： Tahmasebi, Shirin Safi, Mohadeseh Zolfi, Somayeh Maghsoudi, Mohammad Reza Faragardi, Hamid Reza Fotouhi, Hossein Sharif Univ Technol Dept Comp Engn Tehran *** Iran Tech & Vocat Univ Shariaty Tech Coll Tehran *** Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran Zand Inst Higher Educ Shiraz *** Iran KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-10044 Stockholm Sweden Malardalen Univ Sch Innovat Design & Engn S-72123 Vasteras Sweden

Due to reliability and performance considerations, employing multiple software-defined networking (SDN) controllers is known as a promising technique in Wireless Sensor Networks (WSNs). Nevertheless, employing multiple controllers increases the inter-controller synchronization overhead. Therefore, optimal placement of SDN controllers to optimize the performance of a WSN, subject to the maximum number of controllers, determined based on the synchronization overhead, is a challenging research problem. In this paper, we first formulate this research problem as an optimization problem, then to address the optimization problem, we propose the cuckoo Placement of Controllers (cuckoo-PC) algorithm. cuckoo-PC works based on the cuckoo optimization algorithm which is a meta-heuristic algorithm inspired by nature. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. To evaluate the performance of cuckoo-PC, we compare it against a couple of state-of-the-art methods, namely Simulated Annealing (SA) and Quantum Annealing (QA). The experiments demonstrate that cuckoo-PC outperforms both SA and QA in terms of the network performance by lowering the average distance between sensors and controllers up to 13% and 9%, respectively. Comparing our method against Integer Linear Programming (ILP) reveals that cuckoo-PC achieves approximately similar results (less than 1% deviation) in a noticeably shorter time.

关键词： wireless sensor networks software defined networks controller node placement cuckoo optimization algorithm synchronization cost

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ANN-cuckoo optimization Technique to Predict Software Cost Estimation

ANN-Cuckoo Optimization Technique to Predict Software Cost E...

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Conference on Information and Communication Technology (CICT)

作者： Desai, Vishnu Sai Mohanty, Ramakanta Keshav Mem Inst Technol Dept Comp Sci Engn Hyderabad Telangana India

ISBN: (纸本)9781538682159

Software cost is of the most complex and vital aspect in consideration when software is in its development stages. To determine the amount of time, effort and resources required to complete the project successfully translate to Software Cost Estimation (SCE). Thus far, many models have been suggested such as Fuzzy Logic, Neural Networks, Support Vector Machines, Ant Colony optimization, Genetic algorithms, Decision Trees, Case-Based Reasoning and Soft Computing Techniques. Such computational models have contributed to a large extent in this arena. Yet, there still lies immense scope to apply optimization methods. Neural Networks are the most utilized techniques in software cost estimation by researchers. In this paper, we propose the use of a new model, i.e. Artificial Neural Networks (ANN) trained using cuckoo optimization algorithm (COA) to predict Software Cost Estimation. The key goal is to exhibit use of a novel learning procedure for ANN to better predict SCE. The proposed model is verified with the ISBSG dataset and results are compared with existing models. The results shown are in terms of Root Mean Squared Error (RMSE) and Mean Magnitude of Relative Error (MMRE).

关键词： Software Cost Estimation Neural Networks cuckoo optimization algorithm RMSE MMRE

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An algorithm for Solving Generalized Single Row Facility Layout Problem

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American Journal of Operations Research 2020年第6期10卷 299-320页

作者： Mana Meskar Kourosh Eshghi Department of Industrial Engineering Sharif University of Technology Tehran Iran

Layout design problem is to determine a suitable arrangement for the departments so that the total costs associated with the flow among departments become least. Single Row Facility Layout Problem, SRFLP, is one of the layout problems that have many practical applications. This problem and its specific scenarios are often used to model many of the raised issues in the field of facility location. SRFLP is an arrangement of n departments with a specified length in a straight line so that the sum of the weighted distances between the pairs of departments is minimized. This problem is NP-hard. In this paper, first, a lower bound for a special case of SRFLP is presented. Then, a general case of SRFLP is presented in which some new and real assumptions are added to generate more practical model. Then a lower bound, as well as an algorithm, is proposed for solving the model. Experimental results on some instances in literature show the efficiency of our algorithm.

关键词： Single Row Facility Layout Problem Facility Location Plant Layout optimization cuckoo optimization algorithm

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