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A NEW METHOD OF PARAMETER IDENTIFICATION FOR PROTON EXCHANGE MEMBRANE FUEL CELL BASED ON HYBRID PARTICLE SWARM optimization WITH DIFFERENTIAL EVOLUTION ALGORITHM

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THERMAL SCIENCE 2023年第5B期27卷 4209-4222页

作者： Liu, Dong Yang, Xiangguo Guan, Cong Qi, Tianxi Zheng, Qinggen Wuhan Univ Technol Key Lab High Performance Ship Technol Minist Educ Wuhan Peoples R China

With the characteristics of high energy conversion efficiency, high energy density and low operating temperature, the proton exchange membrane fuel cells (PEMFC) have become one of the green energy sources with broad prospects. The establishment of accurate mathematical model of the PEMFC is the basis of simulation and control strategy. At present, some intelligent algorithms have certain drawbacks, and can hardly find the balanced point between precision and computational time. In this study, a novel parameter identification approach combining the hybrid particle swarm optimization (PSO) algorithm with differential evolution, i.e. hybrid DEPSO, is proposed to obtain the unknown parameters in the PEMFC mathematical model and solve the problems of premature convergence of PSO and poor global search ability of differential evolution. Six benchmark functions are applied to verify the performance of the algorithm. The results prove that the hybrid DEPSO can evade local optimum preferably while having swifter convergence rate. Two PEMFC stacks are investigated and modeled. In order to evaluate the accuracy of model, the sum of squared errors between the measured voltage and the estimated output voltage are examined. Numerical results show higher accuracy of the hybrid DEPSO-based model comparing with other recently published optimization approaches. Furthermore, the simulation results indicate that the accuracy of the PEMFC model optimized by the hybrid DEPSO algorithm improves 0.19-1.86%, which can provide a new solution the multi-objective optimization problem and promote the practical application of the PEMFC.

关键词： PEMFC parameters identification PSO algorithm differential evolution algorithm

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An Improved Surrogate-Assisted multi-objective Evolutionary Algorithm Based on Heterogeneous Ensemble

An Improved Surrogate-Assisted Multi-Objective Evolutionary ...

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第43届中国控制会议

作者： Li Wenlu Guo Nan Qiao Junfei Peng Yixin Liu Jiahui Sun Yueyang Jia Yuxin Information Science Department Beijing University of Technology College of Carbon Neutrality Future Technology Beijing University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the practical industrial field,optimizing parameter configurations during factory operations is an indispensable step,especially for multi-objective optimization problems(MOPs) that involve high computational,economic costs or extensive numerical simulations,which was categorized as Expensive multi-objective optimization Problems(EMOPs).To efficiently and reasonably evaluate fitness in the face of EMOP,it is essential to develop a surrogate model to minimize costly expensive function evaluations and assist the entire optimization *** study proposes a Surrogate-Assisted Evolutionary Algorithm SFEA that integrates Support Vector Machines(SVM) and Feedforward Neural Networks(FNN).SFEA constructs surrogate models using heterogeneous integration methods to mitigate the limitations of a single-model approaches in complex and variable optimization scenarios,thereby enhancing the efficiency and quality of the optimization ***,this paper proposes an adaptive sampling strategy and a corresponding sample filling mechanism within a dual-archive management framework,based on the convergence and diversity indices of the *** validation on multi-objective DTLZ and WFG benchmark problems confirms the effectiveness and feasibility of SFEA in addressing expensive multi-objective optimization challenges.

关键词： Support Vector Machine(SVM) feedforward neural network(FNN) model management surrogate-assisted evolutionary algorithm(SAEA) multi-objective optimization algorithm(MOPs)

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A multi-objective grey wolf optimizer (GWO)-based multi-layer perceptrons (MLPs) trainer for optimal PMUs placement

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2022年第1期41卷 187-208页

作者： Laouid, Abdelkader Azzeddine Mohrem, Abdelkrim Djalab, Aicha Ziane Achour Univ Djelfa Fac Sci & Technol Appl Automat & Ind Diagnost Lab LAADDI Djelfa Algeria Univ MHamed Bougara Res Lab Electrificat Ind Enterprises Boumerdes Algeria Ziane Achour Univ Djelfa Fac Sci & Technol Dept Elect Engn Djelfa Algeria

Purpose This paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the redundancy of measurements, in normal cases (with and without zero injection bus [ZIB]), and then in conditions of a single PMU failure and outage of a single line. Design/methodology/approach An efficient approach operates adequately and provides the optimal solutions for the PMUs placement problem. The finest function of optimal PMUs placement (OPP) should be mathematically devised as a problem, and via that, the aim of the OPP problem is to identify the buses of the power system to place the PMU devices to ensure full observability of the system. In this paper, the grey wolf optimizer (GWO) is used for training multi-layer perceptrons (MLPs), which is known as Grey Wolf Optimizer (GWO) based Neural Network ("GW-NN") to place the PMUs in power grids optimally. Findings Following extensive simulation tests with MATLAB/Simulink, the results obtained for the placement of PMUs provide system measurements with less or at most the same number of PMUs, but with a greater degree of observability than other approaches. practical implications The efficiency of the suggested method is tested on the IEEE 14-bus, 24-bus, New England 39-bus and Algerian 114-bus systems. Originality/value This paper proposes a new method for placing PMUs in the power grids as a multi-objective to reduce the cost and improve the observability of these grids in normal and faulty cases.

关键词： Particle swarm optimization Power transmission systems multi-objective optimization Phasor measurement units (PMUs) Zero injection bus (ZIB) Grey wolf optimizer (GWO) for training multi-layer perceptron (MLP) Redundancy of measurement

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Adaptive surrogate assisted multi-objective optimization approach for highly nonlinear and complex engineering design problems

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APPLIED SOFT COMPUTING 2024年 150卷

作者： Younis, Adel Dong, Zuomin Australian Univ Sch Engn Mech Engn Dept POB 1411 Kuwait 13015 Kuwait Univ Victoria Dept Mech Engn Victoria BC V8P 5C2 Canada

Despite enormous advances in computer power, computationally costly models impede the use of traditional optimization approaches that must be invoked repeatedly during the optimization process in practical engi-neering applications. Surrogate models have been found to be a promising endeavor in multi-objective optimi-zation problems involving expensive analysis and simulation processes such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD. Developing an optimization algorithm that can easily identify the Pareto frontier of highly nonlinear multi-objective optimization problems with less computation cost is the aim of this work. In this paper, an Adaptive multi-objective optimization approach based Surrogate models (AMOS) is developed to reduce computation cost of fitness evaluations and discover the Pareto optima for multi-objective optimization problems with comparable high accuracy. AMOS explores the design space by sampling using LHD to identify promising regions. Then, AMOS exploits the identified promising region by adaptively constructing the most suitable surrogate model, which could be response surface, radial basis, or Kriging surrogates, in the feasible design space based on root mean square error values (RMSE). AMOS stops iterating when a termination criterion is met, and a Pareto frontier is identified based on developed guidance and fitness functions. AMOS has successfully identified the pareto frontier of practical engineering optimization problems with expensive black box functions and significantly reduced the computation cost. The novel method was put to the test utilizing real-world challenges and engineering design examples such vehicle magnetorheological design and wind turbine airfoil geometry.

关键词： multi-objective optimization Surrogate models Pareto frontier Intensive computation Black box Kriging Radial basis function Quadratic response function

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A modified energy management strategy for PV/diesel hybrid system to reduce diesel consumption based on artificial protozoa optimizer

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SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Mohamed, Rania G. Hassan, Amal A. Abdel Aleem, Shady H. E. Inst Aviat Engn & Technol Dept Elect Engn Giza 12658 Egypt Elect Res Inst Dept Photovolta Cells Cairo 11843 Egypt

The photovoltaic (PV)/diesel hybrid system (PV/D-HS) combines solar PV panels with a diesel generator (DG) to meet energy demands, especially in industrial operations. This study introduces an improved energy management strategy designed to optimize the performance of PV/D-HS by reducing diesel consumption, increasing solar energy utilization, and minimizing environmental impact. The strategy dynamically manages power distribution between the PV panels and the DG, adapting to changing solar conditions and energy demands. Doing so reduces the system's reliance on diesel, improves operational efficiency, and supports the integration of cleaner energy sources. Simulation results show significant improvements over traditional approaches: carbon emissions decreased from 62 kg/day with a standalone diesel generator to 38 kg/day, representing a 38% reduction. The solar energy fraction (SEF) increases from 12 to 35%, a 23% improvement in solar energy utilization. These results demonstrate the potential of the proposed strategy to enhance sustainability by lowering greenhouse gas emissions, reducing dependence on fossil fuels, and advancing global efforts to combat climate change. In addition to environmental benefits, the approach reduces operational costs and improves the system's reliability, making it a practical solution for industrial energy needs.

关键词： Artificial protozoa optimizer Modified energy management strategy Hybrid system Photovoltaic Diesel generators multi-objective optimization

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Analysis of a many-objective optimization approach for identifying microservices from legacy systems

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EMPIRICAL SOFTWARE ENGINEERING 2022年第2期27卷 1-31页

作者： Assuncao, Wesley K. G. Colanzi, Thelma Elita Carvalho, Luiz Garcia, Alessandro Pereira, Juliana Alves de Lima, Maria Julia Lucena, Carlos Pontifical Catholic Univ Rio de Janeiro PUC Rio Rio De Janeiro Brazil State Univ Maringa UEM Maringa Parana Brazil Pontifical Catholic Univ Rio de Janeiro Tecgraf Inst Rio De Janeiro Brazil

The expensive maintenance of legacy systems leads companies to migrate such systems to modern architectures. Microservice architectural style has become a trend to modernize monolithic legacy systems. A microservice architecture consists of small, autonomous, and highly-independent services communicating by using lightweight network protocols. To support the designing of microservice architectures, recent studies have proposed either single or multi-objective approaches. In order to improve the effectiveness of existing approaches, we introduced toMicroservices that is a many-objective search-based approach to aid the identification of boundaries among services. In previous studies, we have focused on a qualitative evaluation of the applicability and adoption of the proposed approach from a practical point of view, thus the optimization process itself has not been investigated in depth. In this paper, we extend our previous work by performing a more in-depth analysis of our many-objective approach for microservice identification. We compare our approach against a baseline approach based on a random search using a set of performance indicators widely used in the literature of many-objective optimization. Our results are validated through a real-world case study. The study findings reveal that (i) the criteria optimized by our approach are interdependent and conflicting;and (ii) all candidate solutions lead to better performance indicators in comparison to random search. Overall, the proposed many-objective approach for microservice identification yields promising results, which shed light on insights for further improvements.

关键词： Microservice architecture Many-criteria optimization Industrial case study Performance indicators

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Adaptive Optimal multi-Surface Back-Stepping Sliding Mode Control Design for the Takagi-Sugeno Fuzzy Model of Uncertain Nonlinear System With External Disturbance

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IEEE ACCESS 2022年 10卷 14680-14690页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Fekih, Afef Shiraz Univ Technol Elect Engn Fac Shiraz 715555313 Iran Univ Zanjan Dept Elect Engn Zanjan *** Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA

This paper proposes a novel scheme of the multi-objective robust control design for a class of uncertain nonlinear systems in strict-feedback-form based on Takagi-Sugeno fuzzy model (TSFM). The nonlinear system contains both the matched and the unmatched uncertainties and also subjected to the external disturbances. The TSFM provides the generalization of the linear systems concepts to the nonlinear systems field in a convex framework. First, a new sliding surface is defined using a convex combination of the surfaces which are defined for each fuzzy rule consequence local linear subsystems. Then, their gains are designed optimally via a generalized eigenvalue problem (GEVP). Also, the upper-bounds of the matched and unmatched uncertainties are estimated using the adaptive update laws. The multi-objective control aims not only to satisfy the H-2-optimization performance, but also, alpha-stability region is formulated to improve the transient response performance. The H-2-optimization characterization and alpha-stability design conditions are derived in terms of new linear matrix inequalities (LMIs) conditions. Finally, the effectiveness of the proposed approach is demonstrated by considering a comparative practical example.

关键词： Back-stepping sliding mode control approaches H-2-performance LMI regional pole placement Takagi-Sugeno fuzzy model unknown bounded uncertainty

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A self-organizing map approach for constrained multi-objective optimization problems

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COMPLEX & INTELLIGENT SYSTEMS 2022年第6期8卷 5355-5375页

作者： He, Chao Li, Ming Zhang, Congxuan Chen, Hao Zhong, Peilong Li, Zhengxiu Li, Junhua Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Jiangxi Peoples R China Nanchang Hangkong Univ Key Lab Nondestruct Testing Minist Educ Nanchang 330063 Jiangxi Peoples R China

There exist many multi-objective optimization problems (MOPs) containing several inequality and equality constraints in practical applications, which are known as CMOPs. CMOPs pose great challenges for existing multi-objective evolutionary algorithms (M0EAs) since the difficulty in balancing the objective minimization and constraint satisfaction. Without loss of generality, the distribution of the Pareto set for a continuous m-objective CMOP can be regarded as a piecewise continuous manifold of dimension (m - 1). According to this property, a self-organizing map (SOM) approach for constrained multiobjective optimization problems is proposed in this article. In the proposed approach, we adopt the strategy of two population evolution, in which one population is evolved by considering all the constraints and the other population is used to assist in exploring the areas. In the evolutionary stage, each population is assigned a self-organizing map for discovering the population distribution structure in the decision space. After the topological mapping, we utilize the extracted neighborhood relationship information to generate promising offspring solutions. Afterwards, the neuron weight vectors of SOM are updated by the objective vectors of the surviving offsprings. Through the proposed approach, we can make the population efficiently converge to the feasible region with suitable levels of diversity. In the experiments, we compare the proposed method with several state-of-the-art approaches by using 48 benchmark problems. The evaluation results indicate that the overwhelmingly superior performance of the proposed method over the other peer algorithms on most of the tested problems. The source code is available at https://***/hccccc92918/CMOSMA.

关键词： Constrained multi-objective optimization Self-organizing map Evolutionary algorithms

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multi-objective Branch and Bound based on Decomposition

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2022年第3期142卷 373-381页

作者： Kaho, Tomoki Watanabe, Shinya Sakakibara, Kazutoshi Muroran Institute of Technology 27-1 Mizumoto-cho Hokkaido Muroran050-8585 Japan Toyama Prefectural University 5180 Kurokawa Toyama Imizu939-0398 Japan

Traditional multi-objective branch-and-bound approaches to multi-objective mixed-integer linear programming (MOMILP) problems are very expensive to search due to the huge number of Pareto-optimal solutions. In this research, we propose a practical method of dividing a multi-objective problem into multiple single-objective problems by weight vectors and applying the branch-and-bound method (BB) to each subproblem. The proposed method is named multi-objective branch-and-bound based on decomposition (MOBB/D) because it is a combination of the concept of multi-objective evolutionary algorithm based on decomposition (MOEA/D) and BB. The most important feature of MOBB/D is to obtain many Pareto solutions efficiently by sharing information between nearby subproblems in MOMILP problems. In this paper, we describe an approach to share tables in the simplex method as an example of information. Moreover, MOBB/D can control computation cost for solving MOMILP problems by adjusting the number of obtained Pareto solutions. To verify the effectiveness of the proposed method, we compared the search performance of MOBB/D with and without the use of neighborhood information. © 2022 The Institute of Electrical Engineers of Japan.

关键词： multiobjective optimization

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Solving the Workshop Layout optimization Problem Based on a Constrained multimodal multiobjective Evolutionary Algorithm 6

Solving the Workshop Layout Optimization Problem Based on a ...

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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Yue, Caitong Wu, Wenhao Liang, Jing Yu, Kunjie Lin, Hongyu Zhang, Tuo Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Layout optimization (LO) is gaining increasing attention as competition among enterprises intensifies and the demand for cost reduction and efficiency improvement continues to grow. LO poses its peculiar challenges with Many-dimensions, Many-constraints and Many-optima. Although many approaches have been proposed for various LO applications, they focus only on the diversity and convergence of the objective space, neglecting the diversity of solutions, which is detrimental to solving potential issues. Therefore, this paper proposes a constrained multimodal multiobjective optimization (CMMO) framework based on dual-population multi-task collaborative evolution. This framework assists in solving the workshop layout problem by creating an auxiliary task population and transferring knowledge to support the optimization process. Additionally, the dynamic narrowing of constraint boundaries and gradual expansion of the contrast neighborhood for the auxiliary tasks ensure a high degree of correlation with the main task, continuously providing supplementary evolutionary directions. Ultimately, compared to other constrained multimodal multiobjective algorithms and two classic genetic algorithms, the proposed algorithm proves to be more competitive in practical layout optimization problems.

关键词： Workshop layout optimization evolutionary multitasking auxiliary task

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