A multi-objective differential evolution approach based on ε-elimination uniform-diversity for mechanism design

Gholaminezhad, Iman; Jamali, Ali

doi:10.1007/s00158-015-1275-3

“…In this section, a differential evolution algorithm proposed by Gholaminezhad and Jamali [31] is used for optimization Table 2. Equation (6) gives the considered objective function, which is the total overpotential in the fuel cell and electrolyser modes of operation.…”

Section: Optimization Of Optimum Gp Model Based On Differential Evolumentioning

confidence: 99%

Solar energy generation potential along national highways

Sharma¹,

Harinarayana²

2013

Int J Energy Environ Eng

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Reversible solid oxide cells can provide efficient and cost-effective scheme for electrical-energy storage applications. However, this technology faces many challenges from material development to system-level operational parameters , which should be tackle for practical purposes. Accordingly, this study focuses on developing novel robust artificial intelligence-based blackbox models to optimize operational variables of the system. A genetic-programming algorithm is used for Pareto modeling of reversible solid oxide cells in a multi-objective fashion based on experimental input-output data. The robustness of the obtained optimal model evaluated using Monte Carlo simulations technique. An optimization study adopted to optimize the operating parameters, such as temperature and fuel composition using a differential evolution algorithm. The objective functions that have been considered for Pareto multi-objective modeling process are training error and model complexity. In addition, the discrepancy between maximum and minimum output voltage in the whole operation of the system is chosen as the optimization process objective function. The robustness of the optimal trade-off model is shown in terms of statistical indices for varied uncertainty levels from 1 to 10%. The optimized operational condition based on the suggested model reveals optimal intermediate temperature of 762 °C and fuel mixture of about 29% H 2 , 25% H 2 O, and 14% CO.

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“…Multi-objective optimization algorithms based on evolutionary algorithms [39] have been proposed in the literature, e.g. NSGA-II [16],NSGA-III [40], multi-objective uniform-diversity differential evolution (MUDE) [41], and on-line variable-fidelity meta-model assisted Multi-Objective Genetic Algorithm (OLVFM-MOGA) [42].…”

Section: Multi-objective Learningmentioning

confidence: 99%

A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling With Application in Industry 4.0

Khayyam

¹

,

Jamali

²

,

Bab–Hadiashar

³

et al. 2020

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To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modelling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modelling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. INDEX TERMS Industry 4.0; Big data modelling; Limited data modelling, Multi-objective optimization. NOMENCLATURE CNN

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“…In this section, a differential evolution algorithm proposed by Gholaminezhad and Jamali [31] is used for optimization Table 2. Equation (6) gives the considered objective function, which is the total overpotential in the fuel cell and electrolyser modes of operation.…”

Section: Optimization Of Optimum Gp Model Based On Differential Evolumentioning

confidence: 99%

Multi-objective modeling, uncertainty analysis, and optimization of reversible solid oxide cells

Salehi

¹

,

Gholaminezhad

²

2018

Int J Energy Environ Eng

7

0

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Reversible solid oxide cells can provide efficient and cost-effective scheme for electrical-energy storage applications. However, this technology faces many challenges from material development to system-level operational parameters , which should be tackle for practical purposes. Accordingly, this study focuses on developing novel robust artificial intelligence-based blackbox models to optimize operational variables of the system. A genetic-programming algorithm is used for Pareto modeling of reversible solid oxide cells in a multi-objective fashion based on experimental input-output data. The robustness of the obtained optimal model evaluated using Monte Carlo simulations technique. An optimization study adopted to optimize the operating parameters, such as temperature and fuel composition using a differential evolution algorithm. The objective functions that have been considered for Pareto multi-objective modeling process are training error and model complexity. In addition, the discrepancy between maximum and minimum output voltage in the whole operation of the system is chosen as the optimization process objective function. The robustness of the optimal trade-off model is shown in terms of statistical indices for varied uncertainty levels from 1 to 10%. The optimized operational condition based on the suggested model reveals optimal intermediate temperature of 762 °C and fuel mixture of about 29% H 2 , 25% H 2 O, and 14% CO.

show abstract

A multi-objective differential evolution approach based on ε-elimination uniform-diversity for mechanism design

Cited by 13 publications

References 36 publications

Solar energy generation potential along national highways

Solar energy generation potential along national highways

A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling With Application in Industry 4.0

Multi-objective modeling, uncertainty analysis, and optimization of reversible solid oxide cells

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