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Nonlinear simulating of the Proton Exchange Membrane Fuel Cells utilizing Ridgelet Neural Network optimized using a hybrid form of Northern Goshawk Optimizer

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APPLIED ENERGY 2024年 360卷

作者： Li, Ruiheng Tian, Hao Di, Yi Hossain, Sarmistha Hubei Univ Econ Sch Informat & Engn Wuhan 430205 Hubei Peoples R China Hubei Key Lab Digital Finance Innovat Wuhan 430205 Hubei Peoples R China Hubei Univ Econ Hubei Internet Finance Informat Engn Technol Res C Wuhan 430205 Hubei Peoples R China Chittagong Univ Engn & Technol Chittagong 4349 Bangladesh

The utilization of modeling techniques for Proton Exchange Membrane Fuel Cells (PEMFC) presents a viable and proficient approach for comprehending, enhancing, and increasing the efficiency of these sustainable energy sources. This is of paramount importance in tackling worldwide anxieties regarding ecological contamination and the lessening of non-renewable resources of energy. The authors of this research paper introduce a novel approach for the efficient detection of output voltage in PEMFC. This approach involves the utilization of an optimized Ridgelet Neural Network (RNN) in conjunction with a Hybrid Northern Goshawk optimization (HNGO) algorithm. The primary aim of the proposed approach is to reduce the discrepancy among the determined and anticipated resulted voltages of the PEMFC. This is a vital aspect in increasing the fuel cells' efficacy. The authors conducted simulations to assess the efficacy of the proposed approach. The outcomes designated that the RNN/HNGO method outperforms existing methods when it comes to accuracy in tracking voltage signal predictions. Moreover, the proposed approach yields a comparatively lower level of forecast error compared to previously published studies. The aforementioned results underscored the possibility of utilizing the suggested approach in enhancing the performance of PEMFC through practical means. Additionally, the authors propose that the methodology they have presented has the potential to be expanded to encompass other performance metrics of PEMFCs, including power and efficiency. The authors propose the integration of the RNN/HNGO method with additional optimization techniques or its utilization in hybrid systems as a means of augmenting its performance. The authors concluded by acknowledging the need for additional research to experimentally validate the proposed methodology and optimizing its parameters in order to ensure its reliability and applicability in real-world scenarios.

关键词： Ridgelet Neural Network (RNN) Hybrid Northern Goshawk optimization (HNGO) Output voltage prediction Nonlinear modeling optimization algorithm

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A framework to reduce energy consumption in a press shop floor based on industrializable IIoT (I3oT)

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HELIYON 2024年第9期10卷 e29432页

作者： Peinado-Asensi, Ivan Montes, Nicolas Garcia, Eduardo CEU Univ Univ Cardenal Herrera CEU Dept Math Phys & Technol C San Bartolome 55 Valencia 46115 Spain Ford Spain Poligono Ind Ford S-N Valencia 46440 Spain

This article presents a framework to reduce energy consumption in a floor shop press based on Industrializable Industrial Internet of Things (I3oT). The I3oT proposes the development of IIoT tools using the information available in the system, without adding any additional sensors. Based on this philosophy, we proposed to develop the C360 criterion in our previous works, which allowed to extract all the information available in the stamping presses for the development of I3oT applications. In this article, we propose the development of a framework to optimize the parameters accessible from the C360 criterion for energy saving in the stamping process. Regarding the three parameters that can be modified and that affect energy consumption, that is, counterbalance pressure, tonnage and press speed, we will work with the first two in this paper. At the end of the article, the results obtained from the presses installed at Ford factory in Almussafes (Valencia) are shown based on their adjustment.

关键词： Industrializable IIoT(I3oT) Energy management optimization algorithm Automotive manufacturing Press shop

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Optimizing energy costs and reliability: A multi-objective framework with learning-enhanced manta ray foraging for hybrid PV/battery systems

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ENERGY 2024年 291卷

作者： Alghamdi, Ali S. Majmaah Univ Coll Engn Dept Elect Engn Al Majmaah 11952 Saudi Arabia

This paper presents a multi-objective framework for designing a hybrid photovoltaic energy and battery storage system (PV/Battery) with the aim of minimizing the cost of electricity, loss of energy expectation, and loss of load expectation. Focusing on meeting the energy demand of a commercial complex in Al-Jubail industrial city, Saudi Arabia, utilizing real-world data, the proposed framework employs the multi-objective improved manta ray foraging optimization algorithm in conjunction with fuzzy decision-making. To enhance algorithm performance and prevent premature convergence, a Learning-based Hunting Movement Strategy is incorporated. Results highlight the superior performance of Case 3, achieving an optimal balance between cost and reliability. The obtained values for cost of electricity, loss of energy expectation, and loss of load expectation are 0.2255 $/kWh, 170.67 kWh/yr, and 14 h/yr, respectively. This study emphasizes the strengthened capability of the algorithm, augmented through the Learning-based Hunting Movement Strategy, establishing its superiority over established multi-objective methods with a higher percentage of dominant solutions.

关键词： Hybrid photovoltaic/battery energy system Multi-objective design framework Cost of electricity Reliability Learning-based hunting movement strategy Multi-objective improved manta ray foraging optimization algorithm

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Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems (vol 14, 5434, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-33页

作者： Premkumar, Manoharan Sinha, Garima Ramasamy, Manjula Devi Sahu, Santhoshini Subramanyam, Chithirala Bala Sowmya, Ravichandran Abualigah, Laith Derebew, Bizuwork Department of Electrical & Electronics Engineering Dayananda Sagar College of Engineering Kumaraswamy Layout Bengaluru Karnataka 560078 India Department of Computer Science and Engineering Jain University Ramanagaram Bengaluru Karnataka India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering GMR Institute of Technology Rajam Srikakulam Andhra Pradesh India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk 71491 Tabuk Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia 27500 Petaling Jaya Malaysia College of Engineering Yuan Ze University Taoyuan Taiwan Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Bushira Ethiopia Applied science research center Applied science private university Amman 11931 Jordan Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the gre

关键词： Computational intelligence Data mining Grey wolf optimizer K-means clustering optimization algorithm

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Enhancing computational efficiency in topology-optimized mode converters via dynamic update rate strategies

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SCIENTIFIC REPORTS 2024年第1期14卷 1-14页

作者： Cao, Pengfei Duan, Ning Zhao, Zhikai Yu, Mengqiang Li, Congcong Yuan, Mingrui Cheng, Lin Yan, Ge Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Latitude Design Automat Inc Wuxi 214000 Peoples R China Lanzhou Univ Sch Phys Sci & Technol Lanzhou 730000 Peoples R China Lanzhou Inst Phys Lanzhou 730000 Peoples R China

In the big data era, mode division multiplexing, as a technology for extended channel capacity, demonstrates potential in enhancing parallel data processing capability. Consequently, developing a compact, high-performance mode converter through efficient design methods is an urgent requirement. However, traditional design methodologies for these converters face significant computational complexities and inefficiencies. Addressing this challenge, this paper introduces a novel topology optimization design method for mode converters employing a Dynamic Adjustment of Update Rate (DAUR). This approach markedly reduces computational overhead, accelerating the design process while ensuring high performance and compactness. As a proof-of-concept, an ultra-compact dual-mode converter was designed. The DAUR method demonstrated an 80% reduction in computational time compared to traditional methods, while maintaining a compact design (only 1.4 mu m x 1.4 mu m) and an insertion loss under 0.68 dB across a wavelength range of 1525 nm to 1575 nm. Meanwhile, simulated inter-mode crosstalk remained below - 24 dB across a 40 nm bandwidth. A comprehensive comparison with traditional inverse design algorithms is presented, demonstrating our method's superior efficiency and effectiveness. Our findings suggest that DAUR not only streamlines the design process but also facilitates exploration into more complex micro-nano photonic structures with reduced resource investment.

关键词： Mode division multiplexing Integrated optics Topology optimization Mode converter optimization algorithm Ultra compact design

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A parametric approach for performance optimization of residential building design in Beijing

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建筑模拟（英文版） 2020年第2期13卷 223-235页

作者： Jingyu Zhang Nianxiong Liu Shanshan Wang School of Architecture Tsinghua University Beijing 100084 China

During the early design stage of green residential buildings, there are tremendous potential of using parametric optimization to achieve preferable green performance, such as building energy consumption efficiency, daylighting, ventilation and thermal comfort. Taking residential design features into consideration, this paper presents an optimization workflow and effects based on a case study of a residential building project in Beijing. Firstly, 27 design parameters related to residential spatial form and building envelope were selected for the optimization. The simulation results of the cooling and heating load were taken as the optimization objects. Secondly, optimized schemes were obtained from 6246 simulation results, with 1925 verified simulation results proving that the optimized result is reliable. Finally, analysis was performed to establish the correlations between design parameters and performance in order to create the easy access for architects to determine design parameters depending on the performance sensitivity of each parameter. Analysis results showed that parametric optimization of spatial form and building envelope at the design stage is a feasible approach to reducing energy consumption in residential building design.

关键词： residential building design performance optimization parametric design optimization algorithm residential energy conservation

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A novel meta-heuristic optimization method based on golden ratio in nature

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SOFT COMPUTING 2020年第2期24卷 1117-1151页

作者： Nematollahi, Amin Foroughi Rahiminejad, Abolfazl Vahidi, Behrooz Amirkabir Univ Technol Dept Elect Engn Tehran *** Iran Esfarayen Univ Technol Dept Elect & Comp Sci Esfarayen *** North Khorasan Iran Amirkabir Univ Technol Dept Elect Engn Tehran ***1 Iran

A novel parameter-free meta-heuristic optimization algorithm known as the golden ratio optimization method (GROM) is proposed. The proposed algorithm is inspired by the golden ratio of plant and animal growth which is formulated by the well-known mathematician Fibonacci. He introduced a series of numbers in which a number (except the first two numbers) is equal to the sum of the two previous numbers. In this series, the ratio of two consecutive numbers is almost the same for all the numbers and is known as golden ratio. This ratio can be extensively found in nature such as snail lacquer part and foliage growth of trees. The proposed approach employed this golden ratio to update the solutions in an optimization algorithm. In the proposed method, the solutions are updated in two different phases to achieve the global best answer. There is no need for any parameter tuning, and the implementation of the proposed method is very simple. In order to evaluate the proposed method, 29 well-known benchmark test functions and also 5 classical engineering optimization problems including 4 mechanical engineering problems and 1 electrical engineering problem are employed. Using several test functions, the performance of the proposed method in solving different problems including discrete, continuous, high dimension, and high constraints problems is testified. The results of the proposed method are compared with those of 11 well-regarded state-of-the-art optimization algorithms. The comparisons are made from different aspects such as the final obtained answer, the speed and behavior of convergence, and CPU time consumption. Superiority of the purposed method from different points of views can be concluded by means of comparisons.

关键词： Meta-heuristic Golden ratio optimization method optimization algorithm Constrained optimization optimization

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Moth-Flame optimization-Bat optimization: Map-Reduce Framework for Big Data Clustering Using the Moth-Flame Bat optimization and Sparse Fuzzy C-Means

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BIG DATA 2020年第3期8卷 203-217页

作者： Ravuri, Vasavi Vasundra, S. VNRVJIET Hyderabad 500090 Telangana India JNTUA Univ Dept CSE Ananthapuramu India JNTUA Univ Ananthapuramu India

The technical advancements in big data have become popular and most desirable among users for storing, processing, and handling huge data sets. However, clustering using these big data sets has become a major challenge in big data analysis. The conventional clustering algorithms used scalable solutions for managing huge data sets. Thus, this study proposes a technique for big data clustering using the spark architecture. The proposed technique undergoes two steps for clustering the big data, involving feature selection and clustering, performed in the initial cluster nodes of spark architecture. At first, the initial cluster nodes read the big data from various distributed systems, and the optimal features are selected and placed in the feature vector based on the proposed moth-flame optimization-based bat (MFO-Bat) algorithm, which is designed by integrating MFO and Bat algorithms. Then, the selected features are fed to the final cluster nodes of spark, which uses the sparse-fuzzy C-means method for performing optimal clustering. The performance of proposed MFO-Bat outperformed other existing methods with a maximal classification accuracy of 95.806%, Dice coefficient of 99.181%, and Jaccard coefficient of 98.376%, respectively.

关键词： big data big data clustering fuzzy optimization algorithm spark architecture

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A fast and efficient discrete evolutionary algorithm for the uncapacitated facility location problem

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartB期213卷

作者： Zhang, Fazhan He, Yichao Ouyang, Haibin Li, Wenben Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China

In order to solve the uncapacitated facility location problem (UFLP) quickly and effectively, an enhanced group theory-based optimization algorithm (EGTOA) is proposed in this paper. Firstly, a new local search operator, One Direction Mutation Operator, is proposed, which is suitable for solving UFLP. Secondly, a Redundant Checking Strategy is presented to further optimize the quality of feasible solutions. To verify the performance of EGTOA, 15 benchmark instances of UFLP is selected in OR-Library, the comparison results with the 16 existing algorithms show that the solution obtained by EGTOA is better than other algorithms, moreover its speed is much faster than state-of-the-art algorithms. These demonstrates that EGTOA is a fast and effective algorithm for solving UFLP.

关键词： Evolutionary algorithm Facility location problem optimization algorithm One direction mutation operator Redundant checking strategy

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Efficient feature selection for histopathological image classification with improved multi-objective WOA

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Sharma, Ravi Sharma, Kapil Bala, Manju Delhi Technol Univ New Delhi 110042 India Univ Delhi Indraprastha Coll Women Civil Lines New Delhi 110054 India

The difficulty of selecting features efficiently in histopathology image analysis remains unresolved. Furthermore, the majority of current approaches have approached feature selection as a single objective issue. This research presents an enhanced multi-objective whale optimisation algorithm-based feature selection technique as a solution. To mine optimal feature sets, the suggested technique makes use of a unique variation known as the enhanced multi-objective whale optimisation algorithm. To verify the optimisation capability, the suggested variation has been evaluated on 10 common multi-objective CEC2009 benchmark functions. Furthermore, by comparing five classifiers in terms of accuracy, mean number of selected features, and calculation time, the effectiveness of the suggested strategy is verified against three other feature-selection techniques already in use. The experimental findings show that, when compared to the other approaches under consideration, the suggested method performed better on the assessed parameters.

关键词： Image classification Multi-objective grey wolf optimizer optimization algorithm Pre processing

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