Lightweight Chassis Design of Hybrid Trucks Considering Multiple Road Conditions and Constraints

De, Shuvodeep; Singh, Kamaldeep; Seo, Junhyeon; Kapania, Rakesh K.; Ostergaard, Erik; Angelini, Nicholas; Aguero, Raymond

doi:10.3390/wevj12010003

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…Verification and Validation is a very important and essential field where the computational models [1][2][3][4][5][6][7][8][9][10][11][12][13][14] are tested for their credibility. Computer simulations are used to predict real-life outcomes, control vehicles precisely, design light-weight structures etc.…”

Section: Assumptions In Computational Modelsmentioning

confidence: 99%

Verification and Validation in Computational Mechanics

De¹

2022

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Decades ago, when computational was expensive and limited, the structural design was mostly performed by hand calculations using simple mathematical models. For example, it was a common practice to design a structure as complex as the wing of an aircraft by simple beam analysis. However, ever since the classic paper by Turner et al., due to a rapid increase in computational, more complex mathematical models are being used to simulate the physical behavior of complex structural components. To solve intractable problems unsolvable by hand calculations, numerical techniques like Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Finite Difference Method etc. are being employed. In fact, the availability of these methods has led to the development of an entirely new area of research known as Multidisciplinary Design Optimization (MDO) where various disciplines are considered in an optimization problem. The most important question while using a mathematical model to represent practical industrial problems is to what extent these models represent the real-life situation. Computational models are always built on upon assumptions. Simply at looking at the simulation outcomes i.e. the graphical and numerical results, it is often very difficult to ensure if the underlying assumption holds and that the results are reliable. This has led to the development of another field of research known as Verification and Validation (called V&V in short).

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Section: Assumptions In Computational Modelsmentioning

confidence: 99%

Verification and Validation in Computational Mechanics

De¹

2022

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“…The control parameter adaption is enlivened from the success history-based hyperparameter adaptation [25], [34], [35]. It keeps a memory archive of the effective DE hyperparameters, to be specific, scaling factor F in Eq (12) and crossover rate Cr in Eq (13). Success history based memory archives are allotted as in standard SHADE [35] to both the scaling factor F i as S F,i = F i , i = 1, .…”

Section: A Imode Algorithmmentioning

confidence: 99%

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

Saha¹,

De²

2022

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Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

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“…At whatever point the condition N P G < N P G−1 happens, the most exceedingly terrible N P G−1 − N P G individuals are taken out from the population Whereas the non-linear population size reduction (NLPSR) in APGSK is implemented as Eq (13):…”

Section: Population Updatementioning

confidence: 99%

CHAGSKODE Algorithm for Solving Real World Constrained Optimization Problems

Saha¹,

Sallam²,

De³

et al. 2022

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Real-world optimization problems are often gov- erned by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Opti- mization Problems (COPs) fueled by advances in computational power. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose a novel hybrid Evolutionary Algorithm based on the Differential Evolution algorithm and Adaptive Parameter Gaining Sharing Knowledge-based algo- rithm to solve global real-world constrained parameter space. The proposed CHAGSKODE algorithm leverages the power of multiple adaptation strategies concerning the control parameters, search mechanisms, as well as uses knowledge sharing between junior and senior phases. We test our method on the benchmark functions taken from the CEC2020 special session & competition on real-world constrained optimization. Experimental results indicate that CHAGSKODE is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

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Lightweight Chassis Design of Hybrid Trucks Considering Multiple Road Conditions and Constraints

Cited by 8 publications

References 19 publications

Verification and Validation in Computational Mechanics

Verification and Validation in Computational Mechanics

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

CHAGSKODE Algorithm for Solving Real World Constrained Optimization Problems

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