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Sizing of a Battery Pack Based on Series/Parallel Configurations for a High-Power Electric Vehicle as a constrained optimization problem

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第12期69卷 14150-14159页

作者： Pelletier, Louis LeBel, Felix-Antoine Antunes, Carlos Henggeler Trovao, Joao Pedro F. Ctr Technol Avancees BRP UdeS Sherbrooke PQ J1K 0A5 Canada Univ Coimbra INESC Coimbra Dept Elect & Comp Engn P-3030290 Coimbra Portugal Univ Sherbrooke Dept Elect Engn & Comp Engn Sherbrooke PQ J1K 2R1 Canada INESC Coimbra P-3030290 Coimbra Portugal

The design of electric vehicle batteries is a challenge requiring significant computational, human and material resources. This paper proposes a new framework to design a battery pack that allows quick iterations between designs and computes optimized configurations to give more information about the best available solutions. The battery pack is represented as a series/parallel configuration in a Constraint Satisfaction problem (CSP) that is solved to exploit every possible configuration for specific vehicle requirements. The result is then used in a multi-objective constrained optimization problem (COP) to determine the non-dominated (Pareto optimal) solutions according to selected objectives functions. Non-dominated solutions are computed by optimizing weighted-sum scalar functions as well as minimizing different types of distances to a design reference. A case study is explored to design a battery pack for an electric motorcycle. A 24s/6p battery module designed with this methodology has been validated by experiments and the battery pack has successfully been used at MotoStudent in 2018.

关键词： Battery design constrained optimization problem constraint satisfaction problem electric vehicles multi-objective optimization

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Building Correct Estimation Algorithms as a constrained optimization problem

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2008年第3期48卷 500-506页

作者： Smetanin, A. Yu. Russian Acad Sci Dorodnicyn Comp Ctr Moscow 119333 Russia

For estimation algorithms, the problem of building correct algorithms by modifying weights of features and weights of objects is examined. Criteria for the possibility to build a correct algorithm are obtained for certain cases. Conditions of the possibility to build a correct algorithm are obtained in terms of solving a constrained optimization problem. An optimization method is proposed. Under these conditions, the proposed method significantly reduces the computational complexity of synthesizing a correct algorithm.

关键词： estimation algorithm constrained optimization problem system of nonlinear algebraic equations

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A Differential Evolution Algorithm with Adaptive Strategies for constrained optimization problem 16

A Differential Evolution Algorithm with Adaptive Strategies ...

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16th International Conference on Computational Intelligence and Security (CIS)

作者： Wanma, Cuo Li, Hecheng Song, Erping Qinghai Normal Univ Sch Math & Stat Xining 810016 Peoples R China Qinghai Normal Univ Sch Comp Sci & Technol Xining 810016 Peoples R China

ISBN: (纸本)9781665404457

constrained optimization problems are widely used in real-world applications as optimization models. Due to the complexity of the objective itself as well as too tight constraints, it is difficult to obtain the global optimal solution to these problems. In this manuscript, an improved differential evolutionary algorithm is proposed from the perspective of operator design and constraint handling. Firstly, in order to enhance the exploration ability of the algorithm, a heuristic mutation operator with better point information is constructed Secondly, an improved dynamic epsilon constraint handling method is developed, in which the value of the epsilon decreases as the iteration number increases. The method can increase effectively the feasible individual in populations. Finally, the simulation results on 10 benchmark functions show that the proposed algorithm is effective and robust when compared with similar algorithms.

关键词： constrained optimization problem Differential evolution algorithm Constraint handling technique Optimal solutions

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Real-coded genetic algorithm for constrained optimization problem

Real-coded genetic algorithm for constrained optimization pr...

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5th International Conference on Machine Learning and Cybernetics

作者： Zhang, Guo-Li Li, Geng-Yin Ma, Jian-Wei North China Elect Power Univ Hebei 071003 Baoding Peoples R China

ISBN: (纸本)1424400600

This paper proposes a new constraint direct disposal method (GA-CDD), which need not penalty factor, is a universal method dealing with constraint. In order to increase convergence velocity and get global optimal solution, a skill of reserving some unfeasible solution was introduced. The experiment study has shown that the method proposed in this paper is very effective, especially the optimal solution locating in the border of the feasible region.

关键词： constrained optimization problem genetic algorithm real-coded constraint direct disposal reserving unfeasible solution

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Hybrid firefly algorithm with grouping attraction for constrained optimization problem

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KNOWLEDGE-BASED SYSTEMS 2021年 220卷 106937-106937页

作者： Cheng, Zhiwen Song, Haohao Wang, Jiquan Zhang, Hongyu Chang, Tiezhu Zhang, Mingxin Northeast Agr Univ Coll Engn Harbin 150030 Peoples R China Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China

Firefly algorithm (FA) is a new random swarm search optimization algorithm, which simulates the mutual attraction and movement process of flashing fireflies. The different attraction models in FAs have the different number of fitness comparisons and attractions. Too many attractions may result in search oscillations, too few attractions may lead to premature convergence, and too many fitness comparisons may lead to higher computational time complexity. To avoid above problems, a new grouping attraction model is proposed, which can effectively reduce the number of attractions and fitness comparisons. Inspired by the idea of particle swarm optimization position update, the firefly with higher fitness in the group are added with the guidance of the firefly with best fitness. To reduce the probability of the stagnation occurrence and premature convergence of the FA caused by less attractions and fitness comparisons, combination mutation operator is introduced to FA, which can better balance the exploration and exploitation capabilities of the FA. Therefore, hybrid firefly algorithm with grouping attraction (HFA-GA) is given for constrained optimization. Experiments are conducted using CEC 2017 constrained optimization problems and four practical engineering optimization problems. The results show that the HFA-GA can efficiently improve the quality of solutions. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Firefly algorithm Grouping attraction Combination mutation constrained optimization problem

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REAL-CODED GENETIC ALGORITHM FOR constrained optimization problem

REAL-CODED GENETIC ALGORITHM FOR CONSTRAINED OPTIMIZATION PR...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： GUO-LI ZHANG GENG-YIN LI JIAN-WEI MA North China Electric Power University Hebei Baoding 071003 China

关键词： constrained optimization problem genetic algorithm real-coded constraint direct disposal reserving unfeasible solution

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A constrained optimization reformulation of the generalized Nash equilibrium problem

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2017年第1期20卷 27-34页

作者： Hou, Jian Wen, Zong-Chuan Lai, Jun-Feng Inner Mongolia Univ Technol Coll Management Hohhot 010051 Peoples R China Inner Mongolia Univ Technol Coll Sci Hohhot 010051 Peoples R China

The generalized Nash equilibrium problem is an extension of the Nash equilibrium problem by assuming that each player's feasible set depends on the rival player's strategies. By the Nikaido-Isoda function, we reformulate the generalized Nash equilibrium problem as a constrained optimization problem. This reformulation allows us to apply optimization techniques to the constrained optimization problem in order to solve the generalized Nash equilibrium problem. Conditions for the stationary point to be the global minimum of the constrained optimization problem are also given.

关键词： Generalized Nash equilibrium problem Nikaido-Isoda function constrained optimization problem Stationary point

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CGWRIME: collaboration and competition-boosted RIME optimizer for engineering optimization problems

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JOURNAL OF SUPERCOMPUTING 2025年第2期81卷 1-45页

作者： Wang, Zhen Zhao, Dong Heidari, Ali Asghar Chen, Huiling Liang, Guoxi Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China

RIME, a physics-based heuristic algorithm, simulates the natural phenomenon of rime generation and possesses a robust capacity for global exploration, enabling it to escape local optima. However, testing revealed that RIME suffers from slow convergence during the later stages of evaluation, weak individual exploitation capabilities, and subpar population quality when addressing numerical function optimization problems. This paper proposes a fast-convergence soft-rime search strategy to address these issues by enhancing the soft-rime coefficient, a critical parameter, to mitigate slow convergence in RIME's later evaluation stages. Additionally, the concepts of collaboration and competition, inherent in swarm intelligence-based algorithms, are introduced through the hard-rime puncture strategy, aimed at improving individual exploitation and population quality in RIME. An improved version, termed CGWRIME, is developed by integrating the proposed strategy with a comprehensive learning approach. Subsequently, qualitative analyses and ablation experiments validate the algorithm's search characteristics and the proposed strategy's effectiveness. Comparative experiments with well-known heuristic algorithms and high-performing metaheuristic algorithms confirm CGWRIME's advantages in unconstrained mathematical optimization. Finally, it is applied to five engineering design optimization cases, demonstrating that CGWRIME excels in managing unconstrained numerical functions and provides significant benefits in solving practical engineering optimization problems with constraints.

关键词： constrained optimization problem Engineering optimization problems Metaheuristic algorithms RIME

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A generalized conditional gradient method for multiobjective composite optimization problems

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optimization 2025年第2期74卷 473-503页

作者： Assuncao, P. B. Ferreira, O. P. Prudente, L. F. Univ Fed Goias Inst Matemat & Estat Goiania GO Brazil

This article deals with multiobjective composite optimization problems that consist of simultaneously minimizing several objective functions, each of which is composed of a combination of smooth and non-smooth functions. To tackle these problems, we propose a generalized version of the conditional gradient method, also known as Frank-Wolfe method. The method is analysed with three step size strategies, including Armijo-type, adaptive, and diminishing step sizes. We establish asymptotic convergence properties and iteration-complexity bounds, with and without convexity assumptions on the objective functions. Numerical experiments illustrating the practical behaviour of the methods are presented.

关键词： Conditional gradient method Frank-Wolfe method multiobjective optimization Pareto optimality constrained optimization problem

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A simple method for automatic recreation of railway horizontal alignments

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Railway Engineering Science 2025年第1期33卷 62-78页

作者： Alberte Castro Gerardo Casal Duarte Santamarina Miguel E.Vázquez‑Méndez Transportation and Infrastructure Engineering Universidade de Santiago de CompostelaEPSERúa Benigno Ledo27002 LugoSpain Department of Applied Mathematics Universidade de Santiago de CompostelaEPSERúa Benigno Ledo27002 LugoSpain CITMAga Rúa de Constantino Candeira15782 Santiago de CompostelaSpain

This paper deals with the problem of recreating horizontal alignments of existing railway *** main objective is to propose a simple method for automatically obtaining optimized recreated alignments located as close as possible to an existing *** on a previously defined geometric model,two different constrained optimization problems are *** first problem uses only the information provided by a set of points representing the track centerline while the second one also considers additional data about the existing *** proposed methodology consists of a two-stage process in which both problems are solved consecutively using numerical *** main results obtained applying this methodology are presented to show its performance and to prove its practical usefulness:an academic example used to compare with other methods,and a case study of a railway section located in Parga(Spain)in which the geometry of its horizontal alignment is successfully recovered.

关键词： Railway horizontal alignment Geometrical model constrained optimization problem Numerical resolution

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