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

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.

Keywords

Structure optimization / Genetic algorithm (GA) / Q-learning

References

[1.] Chen X, Wang J, Zhao K, et al. Electric vehicles body frame structure design method: an approach to design electric vehicle body structure based on battery arrangement. Proc Inst Mech Eng Part D J Automob Eng, 2022, 236(9): 2025-2042.

[2.] Mallick PK. Materials, design and manufacturing for lightweight vehicles, 2020, Cambridge: Woodhead Publishing

[3.] Qiu D, Wang Y, Hua W, et al. Reinforcement learning for electric vehicle applications in power systems: a critical review. Renew Sust Energy Rev, 2023, 173: 113052.

[4.] Ahmed M, Zheng Y, Amine A, et al. The role of artificial intelligence in the mass adoption of electric vehicles. Joule, 2021, 5(9): 2296-2322.

[5.] Altun F, Tekin SA, Gürel S et al (2019) Design and optimization of electric cars: a review of technological advances. In: The 8th international conference on renewable energy research and applications (ICRERA). IEEE, Brasov Romania

[6.] Li Q, Wu L, Chen T, et al. Multi-objective optimization design of B-pillar and rocker sub-systems of battery electric vehicle. Struct Multidisc Optim, 2021, 64: 3999-4023.

[7.] Jeong MH, Park GJ. Nonlinear dynamic structural optimization of electric vehicles considering multiple safety tests. Int J Auto Tech, 2023, 24(2): 573-583.

[8.] Wang S, Wang D. Crashworthiness-based multi-objective integrated optimization of electric vehicle chassis frame. Arch Civ Mech Eng, 2021, 21(3): 103.

[9.] De Gaetano G, Mundo D, Maletta C, et al. Multi-objective optimization of a vehicle body by combining gradient-based methods and vehicle concept modeling. Case Stud Mech Syst Signal Process, 2015, 1: 1-7.

[10.] Stabile P, Ballo F, Gobbi M et al (2021) Multi-objective structural optimization of vehicle wheels. In: International design engineering technical conferences and computers and information in engineering conference. ASME, Missouri

[11.] De S, Singh K, Seo J et al (2019) Structural design and optimization of commercial vehicle hassis under multiple load cases and constraints. In: IAA Scitech Forum. San Diego

[12.] Bertolini A, Martins MS, Vieira SM, et al. Power output optimization of electric vehicles smart charging hubs using deep reinforcement learning. Expert Syst Appl, 2022, 201.

[13.] Ryberg AB, Bäckryd RD, Nilsson L. A metamodel-based multidisciplinary design optimization process for automotive structures. Eng Comput, 2015, 31: 711-728.

[14.] Qin H, Guo Y, Liu Z et al (2018) Shape optimization of automotive body frame using an improved genetic algorithm optimizer. Adv Eng Softw 121:235–249

[15.] Shi K, Ruan Z, Jiang Z, et al. Improved plant growth simulation and genetic hybrid algorithm (PGSA-GA) and its structural optimization. Eng Comput, 2018, 35(1): 268-286.

[16.] Chen R, Yang B, Li S, et al. A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem. Comput Ind Eng, 2020, 149: 106778.

[17.] Kimbrough SO, Koehler GJ, Lu M, et al. On a feasible–infeasible two-population (fi-2pop) genetic algorithm for constrained optimization: distance tracing and no free lunch. Eur J Oper Res, 2008, 190(2): 310-327.

[18.] Köksal AE, Li Z, Veeravalli B, et al. Reinforcement learning-enabled genetic algorithm for school bus scheduling. J Intell Transp S, 2022, 26(3): 269-283.

[19.] Karafotias G, Smit SK, Eiben AE (2012) A generic approach to parameter control. In: European conference on the applications of evolutionary computation. Springer, Berlin Heidelberg

[20.] Karafotias G, Hoogendoorn M, Eiben ÁE. Parameter control in evolutionary algorithms: trends and challenges. IEEE T Evolut Comput, 2014, 19(2): 167-187.

[21.] Haasdijk E, Eiben AE, Karafotias G (2010) On-line evolution of robot controllers by an encapsulated evolution strategy. In: IEEE congress on evolutionary computation. IEEE, Barcelona

[22.] Katoch S, Chauhan SS, Kumar V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl, 2021, 80: 8091-8126.

[23.] Lambora A, Gupta K, Chopra K (2019) Genetic algorithm—a literature review. In: International conference on machine learning, big data, cloud and parallel computing (COMITCon), Faridabad, India

[24.] Haldurai L, Madhubala T, Rajalakshmi R. A study on genetic algorithm and its applications. Int J Comput Sci Eng, 2016, 4(10): 139-143.

[25.] Zhang L, Wong TN. An object-coding genetic algorithm for integrated process planning and scheduling. Eur J Oper Res, 2015, 244(2): 434-444.

[26.] Dunjko V, Briegel HJ. Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Rep Prog Phys, 2018, 81(7): 074001.

[27.] Chen Q, Huang M, Xu Q, et al. Reinforcement learning-based genetic algorithm in optimizing multidimensional data discretization scheme. Math Probl Eng, 2020, 2020: 1-13.

[28.] Kober J, Bagnell JA, Peter J. Reinforcement learning in robotics: a survey. Int J Robot Res, 2013, 32(11): 1238-1274.

[29.] Baker BC, Nolan JM, O’Neill B, et al. Crash compatibility between cars and light trucks: benefits of lowering front-end energy-absorbing structure in SUVs and pickups. Accid Anal Prev, 2008, 40(1): 116-125.

[30.] Rajasekaran M, Ram VH, Subramanian M. Multi-objective optimization of material layout for body-in-white using design of experiments. Int J Veh Struct Syst, 2016, 8(1): 17-22.

[31.] Jiang Z, Chen W, Fu Y, et al. Reliability-based design optimization with model bias and data uncertainty. SAE Int J Mater Manuf, 2013, 6(3): 502-516.

[32.] Shi L, Yang RJ, Zhu P. An adaptive response surface method for crashworthiness optimization. Eng Optimiz, 2013, 45(11): 1365-1377.

[33.] Zhan Z, Fu Y, Yang R, et al. On stochastic model interpolation and extrapolation methods for vehicle design. SAE Int J Mater Manuf, 2013, 6(3): 517-531.

[34.] Fang H, Rais-Rohani M, Liu Z, et al. A comparative study of metamodeling methods for multi-objective crashworthiness optimization. Comput Struct, 2005, 83(25/26): 2121-2136.

[35.] Yang J, Zhan Z, Zheng K, et al. Enhanced similarity-based metamodel updating strategy for reliability-based design optimization. Eng Optimiz, 2016, 48(12): 2026-2045.

[36.] Ding R, Lin DK, Wei D. Dual-response surface optimization: a weighted MSE approach. Qual Eng, 2004, 16(3): 377-385.

Funding

State Key Laboratory of Vehicle NVH and Safety Technology http://dx.doi.org/10.13039/501100011331(SKLNVH-2305); Chongqing Jiaotong University http://dx.doi.org/10.13039/501100011317(JDLHPYJD2021008)