Improved genetic algorithm based on reinforcement learning for electric vehicle front-end structure optimization design

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Advances in Manufacturing Pub Date : 2024-07-15 DOI:10.1007/s40436-024-00495-z
Feng-Yao Lyu, Zhen-Fei Zhan, Gui-Lin Zhou, Ju Wang, Jie Li, Xin He
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Abstract

The structural optimization of electric vehicles involves numerous design variables and constraints, making it a complex engineering optimization task over the past decades. Many population-based evolutionary algorithms encounter issues such as converging to local optima and lacking population diversity when tackling such optimization problems. Consequently, the solutions obtained for the optimization may be flawed or suboptimal. To address these problems, an improved genetic algorithm (GA) based on reinforcement learning is proposed in this paper. The proposed method introduces a population delimitation method based on individual fitness ranking. The population is divided into two parts: the excellent population and the ordinary population, and different selection and cross-mutation methods are applied to each part separately. More efficient crossover and mutation methods are then applied to the ordinary population to enhance the generation of excellent individuals. Furthermore, the proposed approach replaces the traditional fixed crossover and mutation rates with a dynamic selection method based on reinforcement learning to enhance optimization efficiency. A markov decision process model is constructed based on GA environment in this context. The population state determination method and reward method are designed for reinforcement learning in the GA environment, dynamically selecting the most appropriate genetic parameters based on the current state of the population. Finally, the uncertainty in the manufacturing process is introduced into the optimization problem and the case study results demonstrate that the proposed reinforcement learning-based GA significantly outperforms other evolutionary algorithms when applied to solving the structural optimization of electric vehicles.

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基于强化学习的改进遗传算法在电动汽车前端结构优化设计中的应用
电动汽车的结构优化涉及众多设计变量和约束条件,因此在过去几十年中一直是一项复杂的工程优化任务。许多基于种群的进化算法在处理此类优化问题时会遇到收敛到局部最优和缺乏种群多样性等问题。因此,优化获得的解决方案可能存在缺陷或次优。为了解决这些问题,本文提出了一种基于强化学习的改进遗传算法(GA)。该方法引入了一种基于个体适应度排名的种群划分方法。种群被分为优秀种群和普通种群两部分,每部分分别采用不同的选择和交叉突变方法。然后将更有效的交叉和突变方法应用于普通种群,以提高优秀个体的生成。此外,提出的方法还用基于强化学习的动态选择方法取代了传统的固定交叉率和突变率,以提高优化效率。在此背景下,基于 GA 环境构建了一个马尔可夫决策过程模型。针对 GA 环境下的强化学习,设计了种群状态确定方法和奖励方法,根据种群的当前状态动态选择最合适的遗传参数。最后,在优化问题中引入了制造过程中的不确定性,案例研究结果表明,在应用基于强化学习的 GA 解决电动汽车结构优化问题时,所提出的 GA 明显优于其他进化算法。
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来源期刊
Advances in Manufacturing
Advances in Manufacturing Materials Science-Polymers and Plastics
CiteScore
9.10
自引率
3.80%
发文量
274
期刊介绍: As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.
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