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11th International Conference on Hybrid Artificial Intelligence Systems (HAIS)

作者： Echevarria, Yuviny Sanchez, Luciano Blanco, Cecilio Univ Oviedo Dept Comp Sci Gijon Spain Univ Oviedo Dept Elect Engn Gijon Spain

ISBN: (纸本)9783319320342;9783319320335

The identification of intelligent models of Li-Ion batteries is a major issue in Electrical Vehicular Technology. On the one hand, the fitness of such models depends on the recursive evaluation of a set of nonlinear differential equations over a representative path in the state space, which is a time consuming task. On the other hand, battery models are intrinsically unstable, and small differences in the initial state or the system, as well as imprecisions in the parameter values, may trigger large differences in the output. Hence, learning battery models from data is a complex multi-modal problem and the parameters of these models must be determined with a high accuracy. In addition to this, producing a dynamical model of a battery is a multi-criteria problem, because the predictive capabilities of the model must be estimated in both the voltage and the temperature domains. In this paper, a selection of state-of-the-art multi-objective optimization algorithms (SPEA2, NSGA-II, OMOPSO, NSGA-III and MOEA/D) are assessed with regard to their suitability for identifying a model of a Li-Ion battery. The dominance relations that occur between the Pareto fronts are discussed in terms of binary additive epsilon-quality indicators. It is concluded that each of the standard implementations of these algorithms has different issues with this particular problem, MOEA/D and NSGA-III being the best overall alternatives.

关键词： multi-objective optimization algorithms Li-Ion battery model Parametric identification

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An effective emergency logistics scheduling model based on multi-objective optimization algorithms 4

An effective emergency logistics scheduling model based on m...

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4th ACM SIGSPATIAL International Workshop on Safety and Resilience (EM-GIS)

作者： Ding, Zhiming Cao, Yang Chi, Yuanying Li, Xuyang Yao, Rui Guo, Limin Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Beijing Univ Technol Coll Econ & Management Beijing Peoples R China

ISBN: (纸本)9781450360449

In recent years, frequent public emergencies have resulted in heavy casualties and economic losses. In the early stage of emergency rescue, the demand for emergency resources is strictly greater than the supply. As an important part of emergency management, emergency resource scheduling is an important manifestation of its rescue value. At present, Many studies take emergency resource scheduling as a multi-objective optimization problem. They focus on the total emergency cost and does not evaluate the emergency resource allocation according to the actual disaster severity of each emergency demand point. What's more, they do not consider road information. However, in actual, the condition of roads directly affects the emergency cost. To address this problem, we proposed an effective emergency logistics scheduling model based on multi-objective optimization algorithms. We get road information from spatial-temporal trajectory data. The research on emergency resource scheduling in this paper is helpful to achieve greater utility under the condition of limited emergency resources and reduce the loss of public emergencies.

关键词： emergency logistics scheduling multi-objective optimization algorithms road network condition trajectory vector feature

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Performance Evaluation and Comparison of multi - objective optimization algorithms for the Analytical Design of Switched Reluctance Machines

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CES Transactions on Electrical Machines and Systems 2017年第1期1卷 58-65页

作者： Shen Zhang Sufei Li Ronald G.Harley Thomas G.Habetler School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332 USA

This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance *** multi-physics and multi-objective nature of electric machine design problems are discussed,followed by benchmark studies comparing generic algorithms(GA),differential evolution(DE)algorithms and particle swarm optimizations(PSO)on a 6/4 switched reluctance machine design with seven independent variables and a strong nonlinear multi-objective Pareto *** better quantify the quality of the Pareto fronts,five primary quality indicators are employed to serve as the algorithm testing *** results show that the three algorithms have similar performances when the optimization employs only a small number of candidate designs or ultimately,a significant amount of candidate ***,DE tends to perform better in terms of convergence speed and the quality of Pareto front when a relatively modest amount of candidates are considered.

关键词： Design methodology differential evolution(DE) generic algorithm(GA) multi-objective optimization algorithms particle swarm optimization(PSO) switched reluctance machines

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Review of surrogate model assisted multi-objective design optimization of electrical machines: New opportunities and challenges

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2025年 215卷

作者： Liu, Liyang Li, Zequan Kang, Haoyu Xiao, Yang Sun, Lu Zhao, Hang Zhu, Z. Q. Ma, Yiming Hong Kong Univ Sci & Technol Guangzhou Robot & Autonomous Syst Thrust Guangzhou 511458 Peoples R China China Southern Power Grid Co Ltd CSG PGC Energy Storage Res Inst Guangzhou 511499 Peoples R China Univ Leicester Sch Engn Leicester LE1 7RH England Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD England

This paper overviews surrogate model-assisted multi-objective design optimization techniques of electrical machines for efficient, accurate, and robust design optimization to ease design issues due to unprecedentedly increasing machine performance requirements. Firstly, the mechanism of surrogate-assisted modeling is introduced by comparing it with conventional physical modeling approaches. The relevant techniques are then categorized and subsequently reviewed in terms of the design of experiments, surrogate model construction, and multi-objective optimization algorithms. The potential application prospects for machine design optimization are highlighted. Finally, three surrogate-assisted modeling methods, i.e., transfer learning-based models, gradient sampling-based multi-fidelity models, and search space decay-based surrogate models, are quantitively compared by applying them to the design optimization of a five-phase permanent magnet synchronous machine.

关键词： Surrogate model Electrical machine optimization design Design of experiments multi-objective optimization algorithms

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Design and multi-objective optimization of hairpin motor cooling system based on orthogonal design method and composite algorithm

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APPLIED THERMAL ENGINEERING 2025年 259卷

作者： Chen, Song Xu, Pingxiang Liu, Yuxi Chongqing Univ Technol Coll Mech Engn Chongqing 400054 Peoples R China Chongqing Polytech Univ Elect Technol Sch Smart Hlth Chongqing 401331 Peoples R China

The new thermal management structure of the motor is designed to ensure effective heat dissipation while maintaining low pressure loss, reduce the required power of the cooling pump. The dimensions of the new thermal management structure of the motor are optimized through the establishment of a multi-objective optimization platform. Computational Fluid Dynamics (CFD) simulations were conducted through single- factor analysis to compare and preliminarily determine the number of turns in the spiral channel. The key structural dimensions of the flow channel were adjusted using orthogonal design method for CFD simulations. The obtained complete orthogonal table was used as a dataset for comparing and analyzing the optimization of the Backpropagation (BP) neural network using the Genetic Algorithm (GA) and the Particle Swarm optimization (PSO) algorithm. Results showed a higher fitting accuracy after optimization with the Particle Swarm optimization algorithm. The Non-dominated Sorting Genetic Algorithm III (NAGA III) was employed for multi-objective optimization of the key flow channel dimensions. The optimal solution identified from the Pareto optimal solution set graph, which is the lowest point combining the highest stator temperature and maximum pressure in the flow channel, was verified through simulation to have a prediction accuracy of 99% for the comprehensive optimization algorithm. Furthermore, the simulation results of the cooling system obtained from multi-objective optimization were compared with the original cooling system, the comparison results show that the maximum pressure has decreased by 43.13%. Orthogonal design methods and composite algorithms are employed to replace traditional optimization methods for multi-objective optimization of motor cooling system in this study, offers a new perspective for the structural design of motor cooling systems.

关键词： Hairpin motor Oil cooling system Orthogonal design method Backpropagation neural network multi-objective optimization algorithms

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multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms

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ADVANCED ENGINEERING INFORMATICS 2019年 40卷 93-109页

作者： Si, Binghui Wang, Jianguo Yao, Xinyue Shi, Xing Jin, Xing Zhou, Xin Southeast Univ Sch Architecture 2 Si Pai Lou Nanjing 210096 Jiangsu Peoples R China Minist Educ Key Lab Urban & Architectural Heritage Conservat Beijing Peoples R China

While optimization studies focusing on real-world buildings are somewhat limited, many building optimization studies to date have used simple hypothetical buildings for the following three reasons: (1) the shape and form of real buildings are complex and difficult to mathematically describe;(2) computer models built based on real buildings are computationally expensive, which makes the optimization process time-consuming and impractical and (3) although algorithm performance is crucial for achieving effective building performance optimization (BPO), there is a lack of agreement regarding the proper selection of optimization algorithms and algorithm control parameters. This study applied BPO to the design of a newly built complex building. A number of design variables, including the shape of the building's eaves, were optimized to improve building energy efficiency and indoor thermal comfort. Instead of using a detailed simulation model, a surrogate model developed by an artificial neural network (ANN) was used to reduce the computing time. In this study, the performance of four multi-objective algorithms was evaluated by using the proposed performance evaluation criteria to select the best algorithm and parameter values for population size and number of generations. The performance evaluation results of the algorithms implied that NSGA-II (with a population size and number of generations of 40 and 45, respectively) performed the best in the case study. The final optimal solution significantly improves building performance, demonstrating the success of the BPO technique in solving complex building design problems. In addition, the findings on the performance evaluation of the algorithms provide guidance for users regarding the selection of suitable algorithms and parameter settings based on the most important performance criteria.

关键词： Building design optimization Artificial neural network multi-objective optimization algorithms Performance evaluation of algorithms Real-world building design

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Dna coding theory and algorithms

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第6期58卷 1-37页

作者： Xu, Jin Liu, Wenbin Zhang, Kai Zhu, Enqiang Peking Univ Sch Comp Sci Key Lab High Confidence Software Technol Minist Ed Beijing 100871 Peoples R China Beijing Technol & Business Univ Sch Comp & Artificial Intelligence Beijing 100048 Peoples R China Guangzhou Univ Inst Comp Technol Guangzhou 510006 Peoples R China Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430081 Peoples R China

DNA computing is an emerging computational model that has garnered significant attention due to its distinctive advantages at the molecular biological level. Since it was introduced by Adelman in 1994, this field has made remarkable progress in solving NP-complete problems, enhancing information security, encrypting images, controlling diseases, and advancing nanotechnology. A key challenge in DNA computing is the design of DNA coding, which aims to minimize nonspecific hybridization and enhance computational reliability. The DNA coding design is a classical combinatorial optimization problem focused on generating high-quality DNA sequences that meet specific constraints, including distance, thermodynamics, secondary structure, and sequence requirements. This paper comprehensively examines the advances in DNA coding design, highlighting mathematical models, counting theory, and commonly used DNA coding methods. These methods include the template method, multi-objective evolutionary methods, and implicit enumeration techniques.

关键词： DNA computing DNA coding design Combinatorial optimization problems multi-objective optimization algorithms

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multi-objective DSE algorithms' Evaluations on Processor optimization

Multi-objective DSE Algorithms' Evaluations on Processor Opt...

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IEEE International Conference on Intelligent Computer Communication and Processing

作者： Radu CHIS Maria VINTAN Lucian VINTAN Computer Science Department Technical University Computer Science & Electrical Engineering Department "Lucian Blaga" University

ISBN: (纸本)9781479914920

Very complex micro-architectures, like complex superscalar/SMT or multicore systems, have lots of configurations. Exploring this huge design space and trying to optimize multiple objectives, like performance, power consumption and hardware complexity is a real challenge. In this paper, using the multi-objective design space exploration tool FADSE, we tried to optimize the hardware parameters of the complex superscalar Grid ALU Processor. We compared how different heuristic algorithms handle the DSE optimization. Three of these algorithms are taken from the jMetal library (NSGAII, SPEA2 and SMPSO) while the other two, CNSGAII and MOHC were implemented by us. We show that in this huge design space the differences between the best found individuals by every algorithm are very small, only the time in which they got to these solutions differs. In order to accelerate the DSE process we also did a feature selection through machine learning techniques and ran all DSE algorithms again with a smaller number of input parameters.

关键词： Design Space Exploration multi-objective optimization algorithms Genetic algorithms Swarm algorithms Superscalar Processor

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A two-stage multi-Criteria optimization method for service placement in decentralized edge micro-clouds

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 121卷 90-105页

作者： Panadero, Javier Selimi, Mennan Calvet, Laura Marques, Joan Manuel Freitag, Felix Open Univ Catalonia Comp Sci Dept IN3 Barcelona Spain South East European Univ Max van der Stoel Inst Tetovo North Macedonia Valencian Int Univ Valencia Spain Univ Politecn Cataluna Dept Comp Architecture Barcelona Spain

Community networks are becoming increasingly popular due to the growing demand for network connectivity in both rural and urban areas. Community networks are owned and managed at the edge by volunteers. Their irregular topology, the heterogeneity of resources and their unreliable behavior claim for advanced optimization methods to place services in the network. In particular, an efficient service placement method is key for the performance of these systems. This work presents the multi-Criteria Optimal Placement method, a novel and fast two-stage multi-objective method to place services in decentralized community network edge micro-clouds. A comprehensive set of computational experiments is carried out using real traces of ***, which is the largest production community network worldwide. According to the results, the proposed method outperforms both the random placement method used currently in *** and the Bandwidth-aware Service Placement method, which provides the best known solutions in the literature, by a mean gap in bandwidth gain of about 53% and 10%, respectively, while it also reduces the number of resources used. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Service placement Distributed systems Community networks Micro-clouds multi-objective optimization algorithms

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multi-objective optimization of a Fractional-Order Control System for an EMS-Type Maglev Model

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第9期73卷 12652-12667页

作者： Wang, Meiqi Zeng, Siheng He, Yixin Su, Shan Liu, Pengfei Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang 050043 Peoples R China Shijiazhuang Tiedao Univ Sch Mech Engn Shijiazhuang 050043 Peoples R China

This paper presents a multi-objective optimization approach for a fractional-order control system for electric-magnetic suspension (EMS) type magnetic levitation (maglev) models, aiming to optimize the non-linear magnetic buoyancy control parameters. Firstly, a multi-body dynamics model of an EMS-type maglev vehicle with a fractional-order PID control system is developed. To fulfill the multi-objective requirements and optimize the fractional order parameters, an improved multi-objective optimization algorithm is proposed. The Sperling index and the magnitude of the vertical suspension gap variation are employed as the optimization objectives. Then, the aforementioned multi-objective optimization algorithm is applied to the constructed multi-body dynamics model to perform multi-objective optimization of the control parameters. Finally, the Pareto front solution is calculated and the optimal control parameters are obtained. The results demonstrate that the optimized control parameters can significantly reduce the vertical suspension gap and Sperling index during operation, with the reduction in the vertical suspension gap amplitude and its standard deviation being 10 similar to 73%, and the reduction in the Sperling index being 8 similar to 13%.

关键词： EMS-type maglev models fractional order discretization multi-objective optimization algorithms maglev control EMS-type maglev models fractional order discretization multi-objective optimization algorithms maglev control

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