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Metaheuristic Techniques in Enhancing the Efficiency and Performance of Thermo-Electric Cooling Devices

ENERGIES 2017年第11期10卷 1703页

作者： Vasant, Pandian Kose, Utku Watada, Junzo Ton Duc Thang Univ Fac Elect & Elect Engn Modeling Evolutionary Algorithms Simulat & Artifi Ho Chi Minh City Vietnam Univ Teknol Petronas Dept Fundamental & Appl Sci Seri Iskandar 32610 Perak Malaysia Suleyman Demirel Univ Dept Comp Engn TR-32260 Isparta Turkey Univ Teknol Petronas Dept Comp & Informat Syst Seri Iskandar 32610 Perak Malaysia

The objective of this paper is to focus on the technical issues of single-stage thermo-electric coolers (TECs) and two-stage TECs and then apply new methods in optimizing the dimensions of TECs. In detail, some metaheuristics-simulated annealing (SA) and differential evolution (DE)-are applied to search the optimal design parameters of both types of TEC, which yielded cooling rates and coefficients of performance (COPs) individually and simultaneously. The optimization findings obtained by using SA and DE are validated by applying them in some defined test cases taking into consideration non-linear inequality and non-linear equality constraint conditions. The performance of SA and DE are verified after comparing the findings with the ones obtained applying the genetic algorithm (GA) and hybridization technique (HSAGA and HSADE). Mathematical modelling and parameter setting of TEC is combined with SA and DE to find better optimal findings. The work revealed that SA and DE can be applied successfully to solve single-objective and multi-objective TEC optimization problems. In terms of stability, reliability, robustness and computational efficiency, they provide better performance than GA. Multi-objective optimizations considering both objective functions are useful for the designer to find the suitable design parameters of TECs which balance the important roles of cooling rate and COP.

关键词： metaheuristic techniques optimization optimization algorithms single-objective optimization multi-objective optimization artificial intelligence thermos-electric cooling devices

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A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images

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APPLIED SOFT COMPUTING 2019年 85卷 105749-000页

作者： Kamoona, Ammar Mansoor Patra, Jagdish Chandra Univ Kufa Fac Engn Dept Elect Engn Najaf Iraq Swinburne Univ Technol Fac Sci Engn & Technol Melbourne Vic Australia

A good contrast image has a significant role in different image processing applications and computer vision algorithms. One of the most common contrast enhancement approaches is histogram equalization (HE) that enhances the contrast of an image globally. However, it gives rise to some over-enhanced regions, loss of detail information, and enhancement of noise. In order to improve the performance of the HE algorithm, local HE and adaptive HE algorithms have been proposed but with limited success. Recently, an evolutionary algorithm named cuckoo search (CS) algorithm has been employed for automatic image contrast enhancement showing promising performance. In this paper, we propose a novel enhanced cuckoo search (ECS) algorithm for image contrast enhancement. In addition, we propose a new range of search space for the parameters of the local/global enhancement (LGE) transformation that need to be optimized. The proposed ECS algorithm is applied to several low contrast test images and its performance is compared with that of the CS algorithm. Next, we compare the performance of the ECS algorithm with artificial bee colony algorithm using the proposed LGE transformation and a global transformation. In the last stage of performance evaluation, the ECS algorithm is compared with several image enhancement algorithms, namely, HE, CLAHE, Particle Swarm optimization (PSO), CS, modified CS and CS-PSO algorithms. In all cases, we have shown the superiority of the ECS algorithm in terms of several performance measures. (C) 2019 Elsevier B.V. All rights reserved.

关键词： optimization algorithms Image contrast enhancement Cuckoo search algorithm Enhanced cuckoo search algorithm

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Towards optimal task positioning in multi-robot cells, using nested meta-heuristic swarm algorithms

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2021年 71卷 102131-102131页

作者： Mutti, S. Nicola, G. Beschi, M. Pedrocchi, N. Tosatti, L. Molinari Natl Res Council Italy Inst Intelligent Ind Technol & Syst Adv Mfg I-20133 Milan Italy Univ Padua Dept Informat Engn DEI Via Gradenigo 6-A Padua Italy Univ Brescia Dipartimento Ingn Meccan & Ind Via Branze 38 Brescia Italy

While multi-robot cells are being used more often in industry, the problem of work-piece position optimization is still solved using heuristics and the human experience and, in most industrial cases, even a feasible solution takes a considerable amount of trials to be found. Indeed, the optimization of a generic performance index along a path is complex, due to the dimension of the feasible-configuration space. This work faces this challenge by proposing an iterative layered-optimization method that integrates a Whale optimization and an Ant Colony optimization algorithm, the method allows the optimization of a user-defined objective function, along a working path, in order to achieve a quasi-optimal, collision free solution in the feasible-configuration space.

关键词： Multi-robot coordination Motion planning optimization algorithms Industrial robotics

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A cost-benefit secured DAG based analysis of distributed energy generation in urban settings

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SUSTAINABLE CITIES AND SOCIETY 2024年 107卷

作者： Jing, Tianjun Zhang, Liqing Ma, Zhicheng Wang, Weizhou China Agr Univ Beijing 100083 Peoples R China State Grid Gansu Elect Power Res Inst Lanzhou 730070 Gansu Peoples R China State Grid Gansu Elect Power Co Lanzhou 730050 Gansu Peoples R China

This study presents a groundbreaking approach to energy management in urban areas, focusing on smart building frameworks. The proposed method integrates a Directed Acyclic Graph (DAG) architecture to enhance data security, transparency, and integrity within the smart building ecosystem. To optimize the energy management system, advanced algorithms, namely Harmony Search (HS) and Artificial Bee Colony (ABC), are employed. Notably, the HS and ABC algorithms undergo modifications to improve their efficiency and convergence properties. The innovative use of these algorithms aims to achieve global solutions in the complex optimization landscape of energy management. The study explores the synergistic effects of DAG, HS, and ABC, providing a robust and secure solution for urban energy challenges. The proposed framework is designed to address the dynamic nature of smart buildings, allowing for adaptive and efficient energy consumption. The findings contribute to the advancement of sustainable urban development, emphasizing the importance of secure data transactions and optimized energy utilization in the context of smart cities. The modified HS and ABC algorithms play a key role in fine-tuning the energy management process, offering a novel and effective approach to address the evolving energy needs of urban areas.

关键词： Smart buildings Energy management Demand response Secured architecture Time-of-use pricing optimization algorithms

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Fault diagnosis method and application based on unsaturated piecewise linear stochastic resonance

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REVIEW OF SCIENTIFIC INSTRUMENTS 2019年第6期90卷 065112-065112页

作者： Li, Zhixing Liu, Xiandong Han, Songjiu Wang, Jianguo Ren, Xueping Beihang Univ Sch Transportat Sci & Engn Beijing Peoples R China Univ Sci & Technol Inner Mongolia Sch Mech Engn Baotou Peoples R China

Signal detection and processing have become an important way to diagnose mechanical faults. The classical bistable stochastic resonance (CBSR) method for signal detection can become saturated, where the amplitude of the output signal gradually stabilizes at a relatively low level instead of increasing with further increases of the input signal amplitude. This leads to difficulty in extracting the weak signals from strong background noise. We studied a new mechanism based on unsaturated piecewise linear stochastic resonance (PLSR). The piecewise linear potential model has a unique structure, which can independently adjust the barrier height and potential wall inclination, so the piecewise linear potential model has a rich potential structure. The rich potential structure makes the potential model unsaturated, thus ensuring that the output signals increase as the input signals increase. In addition, according to the piecewise linear model, the output signal-to-noise ratio (SNR) of the system is deducted. Analysis of the influence of signal strength, potential parameters, and angular frequency on the SNR shows that the optimal SNR can be obtained by adjusting the potential parameters. We propose a weak signal detection method for bearing fault diagnosis. This method can effectively extract the weak fault signals from rolling bearings in a strong noise background. The simulated and experimental bearing fault signals verify that the proposed PLSR method is superior to the CBSR method.

关键词： Stochastic processes Fourier analysis Nonlinear systems Acoustic noise Signal processing Oscillating flow optimization algorithms Adiabatic theorem Potential energy barrier Frequency spectrum

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ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021年 286卷 112162-112162页

作者： Elzain, Hussam Eldin Chung, Sang Yong Park, Kye-Hun Senapathi, Venkatramanan Sekar, Selvam Sabarathinam, Chidambaram Hassan, Mohamed Pukyong Natl Univ Dept Environm Earth Sci Busan South Korea Alagappa Univ Dept Disaster Management Karaikkudi Tamil Nadu India VO Chidambaram Coll Dept Geol Tuticorin India Kuwait Inst Sci Res Water Res Ctr Kuwait Kuwait King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran Saudi Arabia

The enhanced assessment of groundwater contamination vulnerability is necessary for the management and conservation of groundwater resources because groundwater contamination has been much increased continuously in the world by anthropogenic origin. The purpose of this study is to determine the best model among three ANFIS-MOA models (the adaptive neuro-fuzzy inference system (ANFIS) combined with metaheuristic optimization algorithms (MOAs) such as genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization (PSO)) in assessing groundwater contamination vulnerability at a nitrate contaminated area. The Miryang City of South Korea was selected as the study area because the nitrate contamination was widespread in the city with two functions of urban and rural activities. Eight parameters (depth to water, net recharge, topographic slope, aquifer type, impact to vadose zone, hydraulic conductivity and landuse) were classified into the numerical ratings on basis of modified DRASTIC method (MDM) for the input variables of ANFIS-MOA models. The Original ANFIS, and 3 combined models of ANFIS-PSO, ANFIS-DE and, ANFIS-GA used 95 adjusted vulnerability indices (AVI) as the target data of training (70% data) and testing (30% data) processing. The performance of 4 models was evaluated by mean absolute errors (MAE), root mean square errors (RMSE), correlation coefficients (R), ROC/AUC curves and predicted AVI (PAVI) maps. The statistical results, spatial vulnerability maps and correlation coefficients between PAVIs and nitrate concentrations revealed that the order of model excellence was ANFIS-PSO, ANFIS-DE, ANFIS-GA, and Original ANFIS, and that ANFIS-PSO showed the highest performance in training and testing processing. The performance rates of ANFIS-MOA models were also compared with 10 recent popular worldwide models using the correlation coefficients between PVI and nitrate concentrations, and they were superior to other recent popular mode

关键词： Groundwater contamination vulnerability Nitrate ANFIS-MOA models optimization algorithms Adjusted vulnerability index ANFIS-PSO

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Wide area backup protection scheme for distance relays considering the uncertainty of network protection

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 189卷 106651-106651页

作者： Mousavi, Seyed Ali Esmaeilzadeh Chabanloo, Reza Mohammadi Farrokhifar, Meisam Pozo, David Shahid Beheshti Univ Dept Elect Engn Tehran Iran Skolkovo Inst Sci & Technol Ctr Energy Sci & Technol Moscow Russia

Although local protection methods can perform well at speed, in a real network where network uncertainties are unavoidable, these methods may result in inaccurate performance. Therefore, the use of other methods, such as wide area protection, can lead to more accurate results because of using comprehensive information via telecommunication links. In this paper, a wide area protection algorithm is presented to cover the mal-operation of relays due to the network uncertainties. In this method, an objective function is defined based on the performance status of various zones of distance relays, the values of which will be different for each line. Depending on the position of each relay on the transmission line, weighting factors for objective function are determined based on the optimization algorithm and taking into account the possible uncertainties of the power grid. The proposed method has been implemented on IEEE 9-bus system and has proved high detection precision and performance accuracy.

关键词： Backup protection Distance relay Network uncertainty optimization algorithms Wide area protection

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From ab initio data to high-dimensional potential energy surfaces: A critical overview and assessment of the development of permutationally invariant polynomial potential energy surfaces for single molecules

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JOURNAL OF CHEMICAL PHYSICS 2019年第19期151卷 194111-194111页

作者： Brown, Sandra E. Univ Calif San Diego Dept Chem & Biochem La Jolla CA 92093 USA

The representation of high-dimensional potential energy surfaces by way of the many-body expansion and permutationally invariant polynomials has become a well-established tool for improving the resolution and extending the scope of molecular simulations. The high level of accuracy that can be attained by these potential energy functions (PEFs) is due in large part to their specificity: for each term in the many-body expansion, a species-specific training set must be generated at the desired level of theory and a number of fits attempted in order to obtain a robust and reliable PEF. In this work, we attempt to characterize the numerical aspects of the fitting problem, addressing questions which are of simultaneous practical and fundamental importance. These include concrete illustrations of the nonconvexity of the problem, the ill-conditionedness of the linear system to be solved and possible need for regularization, the sensitivity of the solutions to the characteristics of the training set, and limitations of the approach with respect to accuracy and the types of molecules that can be treated. In addition, we introduce a general approach to the generation of training set configurations based on the familiar harmonic approximation and evaluate the possible benefits to the use of quasirandom sequences for sampling configuration space in this context. Using sulfate as a case study, the findings are largely generalizable and expected to ultimately facilitate the efficient development of PIP-based many-body PEFs for general systems via automation. Published under license by AIP Publishing.

关键词： Machine learning Potential energy surfaces optimization problems Regression analysis optimization algorithms

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A novel approach for improving the performance of deep learning-based state of charge estimation of lithium-ion batteries: Choosy SoC Estimator (ChoSoCE)

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ENERGY 2024年 294卷

作者： Korkmaz, Mehmet Aksaray Univ Dept Elect & Elect Engn Aksaray Turkiye

Deep learning-based (DL) methods have recently come to the forefront among the data-driven models due to their success in capturing the complexities of the battery. Many previous DL-based studies for SoC estimation have almost exclusively focused on improving DL structure by proposing various architectures. Questions regarding the outlier or atypical predictions have yet to be adequately addressed. Furthermore, few works benefit from optimization algorithms to determine the hyperparameters of DL. In this study, we have addressed the problem of how to obtain the hyperparameter of DL and fix the improper DL predictions. To this aim, we used two different optimization algorithms to determine the hyperparameters of DL and proposed a novel algorithm that considers the previous SoC estimations. The algorithm either approves or rejects the DL predictions for the relevant step and offers new values for the rejected ones. The proposed scheme is evaluated using a battery dataset which includes different driving cycles. According to the results, it is observed that the optimized DL outperforms the empirical one by at least 35% in terms of performance indices. Moreover, the proposed novel algorithm successfully integrates into all variations and significantly improves the performance index scores.

关键词： Lithium-ion battery Deep learning Outlier removal optimization algorithms State of charge (SoC) estimation

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Improving estimates for reliability and cost in microgrid investment planning models

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JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 2019年第4期11卷 045302-045302页

作者： Hanna, Ryan Disfani, Vahid R. Haghi, Hamed Valizadeh Victor, David G. Kleissl, Jan Univ Calif San Diego Sch Global Policy & Strategy La Jolla CA 92093 USA IIASA Laxenburg Austria Univ Tennessee Dept Elect Engn Chattanooga TN 37403 USA Univ Calif San Diego Ctr Energy Res La Jolla CA 92093 USA Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA

This paper develops a new microgrid investment planning model that determines cost-optimal investment and operation of distributed energy resources (DERs) in a microgrid. We formulate the problem in a bilevel framework, using particle swarm optimization to determine investment and the DER-CAM model (Distributed Energy Resources Customer Adoption Model) to determine operation. The model further uses sequential Monte Carlo simulation to explicitly simulate power outages and integrates time-varying customer damage functions to calculate interruption costs from outages. The model treats nonlinearities in reliability evaluation directly, where existing linear models make critical simplifying assumptions. It combines investment, operating, and interruption costs together in a single objective function, thereby treating reliability endogenously and finding the cost-optimal trade-off between cost and reliability-two competing objectives. In bench-marking against a version of the DER-CAM model that treats reliability through a constraint on minimum investment, our new model improves estimates of reliability (the loss of load expectation) by up to 600%, of the total system cost by 6%-18%, of the investment cost by 32%-50%, and of the economic benefit of investing 27%-47%. Improvements stem from large differences in investment of up to 56% for natural gas generators, solar photovoltaics, and battery energy storage.

关键词： Batteries Renewable energy optimization algorithms Photovoltaics Monte Carlo methods

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