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A two stage differential evolution algorithm for parameter estimation of proton exchange membrane fuel cell

SCIENTIFIC REPORTS 2025年第1期15卷 1-29页

作者： Aljaidi, Mohammad Agrawal, Sunilkumar P. Jangir, Pradeep Pandya, Sundaram B. Parmar, Anil Arpita Alkoradees, Ali Fayez Trivedi, Bhargavi Indrajit Khishe, Mohammad Zarqa Univ Fac Informat Technol Dept Comp Sci Zarqa 13110 Jordan Govt Engn Coll Dept Met Engn Gandhinagar 382028 Gujarat India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Shri KJ Polytech Dept Elect Engn Bharuch India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India Qassim Univ Appl Coll Unit Sci Res Buraydah Saudi Arabia Vishwakarma Govt Engn Coll Ahmadabad Gujarat India Imam Khomeini Naval Sci Univ Nowshahr Dept Elect Engn Nowshahr Iran Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan City Taiwan Jadara Univ Res Ctr Irbid Jordan

Precise models predicting fuel cell performance under different operating conditions require accurate parameter identification in a proton exchange membrane fuel cell (PEMFC). Most traditional parameter estimation methodologies depend on optimization algorithms which are limited in their efficiency, convergence speed, and robustness. Typically, existing algorithms fail to achieve a balance between precision and computational efficiency, leading to suboptimal modeling of the complex, nonlinear behavior of PEMFCs. In this paper, we present the two-stage differential evolution (TDE) algorithm, which fills these gaps by using a new mutation strategy that improves solution diversity and speeds up convergence. Seven critical unknown parameters (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\xi }_{1},{\xi }_{2},{\xi }_{3},{\xi }_{4},\beta ,{R}_{C},$$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}) in PEMFC models are identified by using the proposed TDE algorithm. The optimization process is to minimize the sum of squared errors (SSE) between the experimentally measured and predicted cell voltages. TDE resulted in a 41% reduction in SSE (minimum SSE of 0.0255 compared to 0.0432), a 92% improvement in maximum SSE, and over 99.97% reduction in standard deviation compared to the HARD-DE algorithm. Furthermore, TDE was shown to be 98% more efficient than HARD-DE, with a runtime of 0.23 s, compared to HARD-DE's runtime of 11.95 s. Extensive testing of these advancements was performed on six commercially available PEMFC stacks over twelve case studies, and I/V and P/V characte

关键词： Parameter estimation Proton exchange membrane fuel cell (PEMFC) Differential evolution evolution algorithm Optimization

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A differential evolution algorithm for numerical optimization based on the cosine-exponential population size adaptive method

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MEMETIC COMPUTING 2025年第2期17卷 1-78页

作者： Zeng, Zhiqiang Liang, Wenyi Gao, Le Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Wuyi Univ Sch Elect & Informat Engn Jiangmen 529020 Peoples R China

The performance of differential evolution algorithms is sensitive to the population size, and most existing population size control methods continuously reduce the population during the iteration process, which decreases the exploration ability and makes it difficult to prevent a premature convergence of the algorithm. To improve the exploration ability of the differential evolution algorithm, this paper proposes a cosine-exponential population size adaptive (CEPSA) method. In the iterative process, CEPSA enables the population size to decrease or increase. The CEPSA periodically enhances the diversity of the population in the iterative process of the algorithm, which improves the exploration ability of the algorithm and prevents it from premature convergence. Based on the CEPSA, this paper proposes a new variant of the differential evolution algorithm, which is known as CEDE. In the experiment, the performance of CEDE was verified via the CEC 2014 and CEC 2017 benchmark test sets and several real-world engineering problems. CEDE was compared with 11 variants of differential evolution and six metaheuristic algorithms. The experimental results show that CEDE was significantly better than the compared algorithms. In addition, we conducted a sensitivity analysis on the parameters of CEDE, and the experimental results show that CEDE was not sensitive to the parameters, indicating that CEDE can be easily applied to various optimization problems.

关键词： Differential evolution Cosine-exponential Population size adaptive evolution algorithm

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Grey prediction evolution algorithm with a dominator guidance strategy for solving multi-level image thresholding

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APPLIED SOFT COMPUTING 2025年 174卷

作者： Yang, Peixin Hu, Zhongbo Zhou, Yang Su, Qinghua Xiong, Wentao Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Hubei Engn Univ Sch Math & Stat Xiaogan Hubei Peoples R China

Multi-level thresholding (MLT) stands as a pivotal method for extracting target information from images. Meta-heuristic algorithms provide an efficient way to implement MLT and retains more research space for accuracy optimization of high-dimensional multi-level thresholding (HDMLT) of images than they do for low-dimensional multi-level thresholding (LDMIT). In order to improve the algorithmic accuracy in solving the high-dimensional problems, a grey prediction evolution algorithm with a dominator guidance strategy (GPEdg) is proposed in this paper. GPEdg employs Otsu's method as its objective function to find the best threshold configuration. The novel operator in the algorithm, i.e., a dominator guidance (dg) strategy, uses a linear combination of three difference vectors to guide the top 50% individuals of populations to learn from the top 20% of them. An efficient balance of search abilities suitable for solving HDMLT problems is expected to be achieved by injecting the local search capability of the dg strategy into GPE's powerful global search capability. Furthermore, a thresholding morphological profile based method (TMP) leverages the thresholding results generated by GPEdg to train a support vector machine (SVM) for hyperspectral image classification. Numerical experiments are conducted for the newly proposed algorithm and five state-of-the-art algorithms on three image datasets to compare the performance in six metrics, i.e., peak signal-to-noise ratio, structural similarity index, features similarity index, objective function value, stability and time consumption. Overall accuracy and average accuracy are tested on two commonly used hyperspectral image data. The results show that GPEdg exhibits outstanding thresholding performance while TMP enhances the classification accuracy of these images. If this paper is accepted, Matlab_codes associated with this paper will be uploaded to https://***/Zhongbo-Hu/Prediction-evolutionary-algorithm-HOMEPAGE

关键词： Multi-level image thresholding evolution algorithm Otsu's method Hyperspectral image classification

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Mossbauer spectrum analysis by evolution algorithm

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION B-BEAM INTERACTIONS WITH MATERIALS AND ATOMS 1997年第4期129卷 527-533页

作者： Klencsar, Z Department of Nuclear Chemistry Eötvös Loránd University P.O. Box 32 H-1518 Budapest 112 Hungary

evolution algorithm has been developed for Mossbauer spectrum analysis. Its global search performance was found superior to conventional numerical optimization methods. Beside its derailed description the main advantages of using the evolution algorithm in the field of Mossbauer spectroscopy are illustrated. (C) 1997 Elsevier Science B.V.

关键词： Mossbauer spectrum numerical optimization genetic algorithm evolution algorithm

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Vector Dominating Multi-objective evolution algorithm for Aerodynamic-Structure Integrative Design of Wind Turbine Blade

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Transactions of Nanjing University of Aeronautics and Astronautics 2016年第1期33卷 1-8页

作者： Wang Long Wang Tongguang Wu Jianghai Ke Shitang Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design Nanjing University of Aeronautics and Astronautics

A novel multi-objective optimization algorithm incorporating vector method and evolution strategies,referred as vector dominant multi-objective evolutionary algorithm(VD-MOEA),is developed and applied to the aerodynamic-structural integrative design of wind turbine blades.A set of virtual vectors are elaborately constructed,guiding population to fast move forward to the Pareto optimal front and dominating the distribution uniformity with high *** comparison to conventional evolution algorithms,VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation when handling complex problems of multi-variables,multi-objectives and *** an example,a 1.5 MW wind turbine blade is subsequently designed taking the maximum annual energy production,the minimum blade mass,and the minimum blade root thrust as the optimization *** results show that the Pareto optimal set can be obtained in one single simulation run and that the obtained solutions in the optimal set are distributed quite uniformly,maximally maintaining the population *** efficiency of VD-MOEA has been elevated by two orders of magnitude compared with the classical *** provides a reliable high-performance optimization approach for the aerodynamic-structural integrative design of wind turbine blade.

关键词： wind turbine multi-objective optimization vector method evolution algorithm

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A novel select-best and prepotency evolution algorithm and its application to develop industrial oxidation reaction macrokinetic model

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COMPUTERS & CHEMICAL ENGINEERING 2006年第5期30卷 807-815页

作者： Yan, XF Zhao, WX E China Univ Sci & Technol Automat Inst Shanghai 200237 Peoples R China Clarkson Univ Dept Chem Engn Potsdam NY 13699 USA Clarkson Univ Ctr Air Resources Engn & Sci Potsdam NY 13699 USA

A novel evolution algorithm with the select-best and prepotency operator (SPO), named select-best and prepotency evolution algorithm (SPEA), is proposed. The main genetic operators of SPEA are the proposed SPO and the uniform mutation operator. The SPO is defined as follows. Every individual in population has the same chance to select the best individual within its neighborhood range (the select-best range.) and produce new individuals through the crossover with the selected individual. Then the best one of two new individuals is selected as one individual of the next generation. With the SPO that preserves the diversity of individuals to avoid premature and can make excellent individuals be selected many a time, SPEA possesses advantages over conventional genetic algorithms. To compare the performances of SPEA with those of the real-coded genetic algorithm (GA), SPEA and the real-coded GA were applied to search the global optimal solution of a benchmark function. The comparison results demonstrated that SPEA spends less CPU time than the real-coded GA, the on-line, off-line, and local searching performances of SPEA are superior to those of the real-coded GA, and the probability of obtaining the global optimal solution for SPEA is larger than that for the real-coded GA. In addition, the relationship between the select-best neighborhood range and the CPU time consumed by SPEA was analyzed as well as the relationship between the select-best neighborhood range and the ratio of obtaining the global optimal solution. The results demonstrated that the proper ratio of the individual number in the select-best neighborhood to that in the population was 6%. Finally, SPEA was applied to develop a macrokinetic model of the industrial oxidation reaction of p-xylene to terephthalic acid (OXTA) in an Amoco reactor. The macrokinetic model based on the intrinsic kinetics model introduced the correction coefficients into the rate constants of the intrinsic kinetics model to indicate

关键词： evolution algorithm genetic algorithm macrokinetic model radial basis functions partial least squares optimization

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Dynamic aperture optimization with diffusion map analysis at CEPC using differential evolution algorithm

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2020年第0期959卷 163517-000页

作者： Wu, Jin Zhang, Yuan Qin, Qing Wang, Yiwei Yu, Chenghui Zhou, Demin Chinese Acad Sci Inst High Energy Phys Key Lab Particle Accelerat Phys & Technol Beijing 100049 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China KEK Tsukuba Ibaraki 3050801 Japan

In storage rings, the dynamic aperture is a key concept of nonlinear beam dynamics to evaluate the overall machine performance. Therefore, its optimization becomes very essential, especially during the design stage of a machine. In recent years, with the progress in the field of parallel computing, various multi-objective optimization algorithms have been widely applied to the dynamic aperture optimization of storage ring light sources. In this paper, an alternative algorithm, so-called differential evolution algorithm is introduced to perform dynamic aperture optimization of a very high energy e(+)e(-) storage ring collider, e.g. Circular Electron Positron Collider (CEPC). A multi-objective optimization code based on the differential evolution algorithm has been developed for this purpose and proved to be effective in increasing the dynamic aperture. In our code, a method called diffusion map analysis, in which the diffusion may come from quantum fluctuation of synchrotron radiation, beamstrahlung effect and nonlinearity in the lattice, is used to set the constraints in the dynamic aperture optimization, which can also help us find solutions with good beam lifetime.

关键词： evolution algorithm Multi-objective optimization CEPC Dynamic aperture Beam lifetime Diffusion map analysis

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A problem evolution algorithm with linear programming for the dynamic facility layout problem-A general layout formulation

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COMPUTERS & OPERATIONS RESEARCH 2017年第Dec.期88卷 187-207页

作者： Xiao, Yiyong Xie, Yue Kulturel-Konak, Sadan Konak, Abdullah Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Penn State Berks Management Informat Syst Reading PA 19610 USA Penn State Berks Informat Sci & Technol Reading PA 19610 USA

Facility layout problems (FLPs) are quite common and important in many industries. This paper presents a mixed integer linear programming (MILP) model for the dynamic facility layout problem, which is a generalization of several special cases of FLPs studied in recent years. A new evolutionary meta-heuristic framework, named as the problem evolution algorithm (PEA), is developed as a general solution approach for FLPs. Computational experiments show that the PEA combined with the linear programming (LP), called PEA-LP in short, performs well in various types of FLPs. In addition, a new polyhedral inner approximation method is proposed based on secant lines for the linearization of the non-linear constraint for department area requirements. This new method guarantees that the actual department area is always greater than or equal to the required area within a given maximum deviation error. Furthermore, two new symmetry-breaking constraints which help to improve the computational efficiency of the MILP model are also introduced. Computational experiments on several well-known problem instances from the literature are carried out to test the DFLP-FZ and the PEA-LP with promising results. (c) 2017 Elsevier Ltd. All rights reserved.

关键词： Facility layout evolution algorithm Mixed integer linear programming Hybrid optimization

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Mathematical formulation and a hybrid evolution algorithm for solving an extended row facility layout problem of a dynamic manufacturing system

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2022年 78卷

作者： Guan, Chao Zhang, Zeqiang Zhu, Lixia Liu, Silu Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China Wenzhou Univ Sch Mech & Elect Engn Wenzhou 325035 Peoples R China

In this study, a dynamic extended row facility layout problem is investigated where the dynamic manufacturing process is considered. This problem involves the rearrangement cost when the location of the department changes over adjacent periods and it is aimed at minimising the sum of total material handling and rearrangement costs. The specific number of rows in each period is not predetermined in this study. Based on the product processing in practice, the essential clearance between departments and floor boundaries is also considered when calculating relative positions and exact locations of departments. A mixed-integer linear programming model is established for this problem. A hybrid evolution algorithm (HEA) that combines the exhaustive pairwise swap and variation operations is proposed to resolve this problem. First, a decoding strategy is designed to represent the department sequence and the optimal exact locations are solved using the linear programming method. Second, an exhaustive pairwise swap operation is addressed to search for the neighbourhood space without repeated calculations. This operation terminates when all swap pairs run out or preferable solution is found. Therefore, the variation operation starts immediately to switch to a new neighbourhood space. The HEA is tested with many instances and its good performance has been proven by comparing its results with existing solutions of fixed-row, flexible-row facility layout problems, and results of exact approach (CPLEX).

关键词： Manufacturing system design Dynamic layout problem Row facility layout problem Mixed-integer linear programming evolution algorithm

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Variable selection by an evolution algorithm using modified Cp based on MLR and PLS modeling: QSAR studies of carcinogenicity of aromatic amines

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2003年第2期375卷 248-254页

作者： Shen, Q Jiang, JH Shen, GL Yu, RQ Hunan Univ Coll Chem & Chem Engn State Key Lab ChemoBiosensing & Chemometr Changsha 410082 Peoples R China

The variable selection in QSAR studies by MLR and PLS modeling has been performed using the evolution algorithm (EA). The Cp statistic has been modified and used as the objective function in the EA search for different combinations of molecular descriptors. For MLR modeling a few information-rich descriptors are selected for model formulation. In PLS modeling, the proposed procedure selects a relatively large number of information-containing descriptors, and a PLS model is formulated based on a few latent variables, which are linear combinations of the selected descriptors. The proposed procedures were used for the prediction of carcinogenicity of aromatic amines.

关键词： modified Cp statistic variable selection evolution algorithm QSAR MLR PLS

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