Multi-objective crashworthiness optimization of vehicle body using particle swarm algorithm coupled with bacterial foraging algorithm

Wang, Dengfeng; Cai, Kefang

doi:10.1177/0954407017724636

Cited by 10 publications

(4 citation statements)

References 58 publications

(78 reference statements)

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“…The following section reviews frameworks and Python libraries, which use optimization algorithms combined with surrogate models. Wang and Chai combine the PSO with a BFO to form the PSO-BFO algorithm [31]. The algorithm uses a Radial Basis Function (RBF) surrogate model to optimize the behavior of a full-frontal impact and side impact of an SFE CONCEPT BIW model.…”

Section: Surrogate Models and Optimization Methodsmentioning

confidence: 99%

Test and Validation of the Surrogate-Based, Multi-Objective GOMORS Algorithm against the NSGA-II Algorithm in Structural Shape Optimization

et al. 2022

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Nowadays, product development times are constantly decreasing, while the requirements for the products themselves increased significantly in the last decade. Hence, manufacturers use Computer-Aided Design (CAD) and Finite-Element (FE) Methods to develop better products in shorter times. Shape optimization offers great potential to improve many high-fidelity, numerical problems such as the crash performance of cars. Still, the proper selection of optimization algorithms provides a great potential to increase the speed of the optimization time. This article reviews the optimization performance of two different algorithms and frameworks for the structural behavior of a b-pillar. A b-pillar is the structural component between a car’s front and rear door, loaded under static and crash requirements. Furthermore, the validation of the algorithm includes a feasibility constraint. Recently, an optimization routine was implemented and validated for a Non-dominated Sorting Genetic Algorithm (NSGA-II) implementation. Different multi-objective optimization algorithms are reviewed and methodically ranked in a comparative study by given criteria. In this case, the Gap Optimized Multi-Objective Optimization using Response Surfaces (GOMORS) framework is chosen and implemented into the existing Institut für Konstruktionstechnik Optimizes Shapes (IKOS) framework. Specifically, the article compares the NSGA-II and GOMORS directly for a linear, non-linear, and feasibility optimization scenario. The results show that the GOMORS outperforms the NSGA-II vastly regarding the number of function calls and Pareto-efficient results without the feasibility constraint. The problem is reformulated to an unconstrained, three-objective optimization problem to analyze the influence of the constraint. The constrained and unconstrained approaches show equal performance for the given scenarios. Accordingly, the authors provide a clear recommendation towards the surrogate-based GOMORS for costly and multi-objective evaluations. Furthermore, the algorithm can handle the feasibility constraint properly when formulated as an objective function and as a constraint.

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Section: Surrogate Models and Optimization Methodsmentioning

confidence: 99%

Test and Validation of the Surrogate-Based, Multi-Objective GOMORS Algorithm against the NSGA-II Algorithm in Structural Shape Optimization

et al. 2022

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“…The loads that can be experienced by a chassis come in different forms such as static loads and dynamic loads. The chassis also experiences longitudinal loads, inertia loads, cornering loads, and vibrational loads (Wang and Cai 2018). A good chassis should be able to withstand the stresses due to these loads that it is expected to experience without undue deflection, distortion, or plastic deformation.…”

Section: )mentioning

confidence: 99%

Structural Analysis of a Lightweight Electric Vehicle Chassis

Omidiji,

Daniyan,

Owolabi

et al. 2024

mijst

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This study presents a comprehensive investigation into the design and structural analysis of lightweight chassis for electric vehicles. A chassis needs to be able to withstand twist, shock, vibration, and other stresses caused by acceleration, braking, road condition, and shock initiated by other parts of a vehicle. It should carry a maximum load under all operating conditions. Two materials, 304L steel, and Ti-6AL-4V alloy were evaluated, with parameters such as total deformation, equivalent stress, and equivalent elastic strain under consideration. The outcome of the Finite Element Analysis revealed that Ti-6AL-4V can withstand higher stresses than 304L Steel. By investigating the static behaviors of the chassis under static loading due to weight and overload conditions, Ti-6AL-4V was chosen as a suitable replacement for a 304L Steel chassis.

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“…For both MCMBFO (Niu, Liu, & Tan, 2019) and MORBCO (Niu et al, 2020), topological communication is employed as the main information transfer path can improve search efficiency to some extent but they neglect to identify what important information should be restored and transferred within the population for tackling MOPs. In Dhillon et al (2016) and Wang and Cai (2018), PSO‐like single global best learning paradigm was also combined with BFO to solve MOPs and applied to the load frequency control problem and crashworthiness optimization of vehicle body respectively. Guo, Tang, and Niu (2021) proposed an evolutionary state‐based novel multiobjective periodic bacterial foraging optimization algorithm, named ES‐NMPBFO.…”

Section: Related Workmentioning

confidence: 99%

An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems

Wang

Liu

et al. 2023

Expert Systems

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The information transfer mechanism within the population is an essential factor for population‐based multiobjective optimization algorithms. An efficient leader selection strategy can effectively help the population to approach the true Pareto front. However, traditional population‐based multiobjective optimization algorithms are restricted to a single global leader and cannot transfer information efficiently. To overcome those limitations, in this paper, a multiobjective bacterial colony optimization with dynamic multi‐leader co‐evolution (MBCO/DML) is proposed, and a novel information transfer mechanism is developed within the group for adaptive evolution. Specifically, to enhance convergence and diversity, a multi‐leaders learning mechanism is designed based on a dynamically evolving elite archive via direction‐based hierarchical clustering. Finally, adaptive bacterial elimination is proposed to enable bacteria to escape from the local Pareto front according to convergence status. The results of numerical experiments show the superiority of the proposed algorithm in comparison with related population‐based multiobjective optimization algorithms on 24 frequently used benchmarks. This paper demonstrates the effectiveness of our dynamic leader selection in information transfer for improving both convergence and diversity to solve multiobjective optimization problems, which plays a significant role in information transfer of population evolution. Furthermore, we confirm the validity of the co‐evolution framework to the bacterial‐based optimization algorithm, greatly enhancing the searching capability for bacterial colony.

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Multi-objective crashworthiness optimization of vehicle body using particle swarm algorithm coupled with bacterial foraging algorithm

Cited by 10 publications

References 58 publications

Test and Validation of the Surrogate-Based, Multi-Objective GOMORS Algorithm against the NSGA-II Algorithm in Structural Shape Optimization

Test and Validation of the Surrogate-Based, Multi-Objective GOMORS Algorithm against the NSGA-II Algorithm in Structural Shape Optimization

Structural Analysis of a Lightweight Electric Vehicle Chassis

An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems

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