Grey wolf algorithm for multidimensional engine optimization of converted plug-in hybrid electric vehicle

Gujarathi, Pritam Keshavdas; Shah, Varsha A.; Lokhande, Makarand M.

doi:10.1016/j.trd.2018.06.003

Cited by 24 publications

(10 citation statements)

References 23 publications

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“…Alpha ( α ) wolves are mainly responsible for making decisions about where the population hunts, where they sleep, when they wake up, and so on. The decision of the alpha ( α ) wolf depends on individual thinking and decision-making behaviour [ 31 ]. However, biologists have also observed some kind of democratic behaviour; that is, alpha ( α ) wolves sometimes follow other subordinate wolves.…”

Section: A New Methodsmentioning

confidence: 99%

Solving the Location Selection Problem of Self‐Service Stores from the Perspectives of Sustainability and Uncertainty

Zhang

Hou

Feng

et al. 2022

Journal of Environmental and Public Health

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This paper presents an improved method for selecting a specific location in the development of convenience stores in municipal areas. This method solves the problem of self-service store location from the perspective of sustainability and uncertainty and adequately considers the characteristics of individual locations with the proposition of an improved grey wolf optimization algorithm. The example presented shows that the improved algorithm has obvious advantages in facilitating the selection of convenience stores with respect to the search precision, stability, and convergence. Based on the macroenvironment, income, and cost, this work establishes a relatively complex, complete, and targeted mathematical model. Finally, taking the Xiaonanzhuang area of Suzhou Street in Beijing as an example, the scientificity, feasibility, and sustainability of the location model are verified.

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Section: A New Methodsmentioning

confidence: 99%

Solving the Location Selection Problem of Self‐Service Stores from the Perspectives of Sustainability and Uncertainty

Zhang

Hou

Feng

et al. 2022

Journal of Environmental and Public Health

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“…The fact that metaheuristic methods give better results than classical methods in many optimization problems that are difficult to solve has increased the interest in these methods in recent years and has led to the emergence of many different metaheuristic methods. [31][32][33][34][35][36][37] As a matter of fact, CSA is a relatively new metaheuristic method inspired by the reproductive strategy of some cuckoos called brood parasitism in 2009. However, studies show that CSA gives much better results than metaheuristic methods such as genetic algorithms (GA) and particle swarm optimization (PSO).…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Characterization modeling of wake vortex circulation decay under reasonable worst case conditions with cuckoo search algorithm

Oruc,

Baklacioglu,

Sahin

2024

Proceedings of the Institution of Mechanical Engineers, Part G:

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Wake vortex (WV) produced by a large aircraft has the potential to cause serious damage to smaller aircraft following it. In this context, characterization of WV circulation decay under the reasonable worst case (RWC) conditions allows the separation minima to be found safely. In this study, modeling of dimensionless decay curves, which were developed using three experimental LIDAR (Light Detection and Ranging) datasets in the RECAT-EU project and is a useful tool to characterize the wake vortex circulation decay under RWC conditions, was carried out using cuckoo search algorithm (CSA). The decay curves used in the modeling are the median (P50), 10th (P10), and 90th (P90) percentile decay curves of the RWC tracks, which constitute the top 2% longest lasting wakes. The fact that the correlation coefficient (R) values are very close to 1 for all datasets as a result of the error analysis shows that the prediction success of the CSA model is quite high.

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“…The highest yield of biodiesel fuel (96.70%) was obtained with a methanol/oil molar ratio of 5.99/1, a catalyst amount of 1.1 wt.%, and a reaction period of 77.6 min. Gujarathi et al [51] employed the GWO to improve the diesel engine's performance and emissions. A wide variety of factors, including BSFC, HC, CO, NOx, and PM, were taken into account throughout the optimization process.…”

Section: Introductionmentioning

confidence: 99%

Environmental Assessment of a Diesel Engine Fueled with Various Biodiesel Blends: Polynomial Regression and Grey Wolf Optimization

Alahmer

Handam

et al. 2022

Sustainability

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A series of tests were carried out to assess the environmental effects of biodiesel blends made of different vegetable oil, such as corn, sunflower, and palm, on exhaust and noise diesel engine emissions. Biodiesel blends with 20% vegetable oil biodiesel and 80% diesel fuel by volume were developed. The tests were conducted in a stationary diesel engine test bed consisting of a single-cylinder, four-stroke, and direct injection engine at variable engine speed. A prediction framework in terms of polynomial regression (PR) was first adopted to determine the correlation between the independent variables (engine speed, fuel type) and the dependent variables (exhaust emissions, noise level, and brake thermal efficiency). After that, a regression model was optimized by the grey wolf optimization (GWO) algorithm to update the current positions of the population in the discrete searching space, resulting in the optimal engine speed and fuel type for lower exhaust and noise emissions and maximizing engine performance. The following conclusions were drawn from the experimental and optimization results: in general, the emissions of unburned hydrocarbon (UHC), carbon dioxide (CO2), and carbon monoxide (CO) from all the different types of biodiesel blends were lower than those of diesel fuel. In contrast, the concentration of nitrogen oxides (NOx) emitted by all the types of biodiesel blends increased. The noise level produced by all the forms of biodiesel, especially palm biodiesel fuel, was lowered when compared to pure diesel. All the tested fuels had a high noise level in the middle frequency band, at 75% engine load, and high engine speeds. On average, the proposed PR-GWO model exhibited remarkable predictive reliability, with a high square of correlation coefficient (R2) of 0.9823 and a low root mean square error (RMSE) of 0.0177. Finally, the proposed model achieved superior outcomes, which may be utilized to predict and maximize engine performance and minimize exhaust and noise emissions.

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Grey wolf algorithm for multidimensional engine optimization of converted plug-in hybrid electric vehicle

Cited by 24 publications

References 23 publications

Solving the Location Selection Problem of Self‐Service Stores from the Perspectives of Sustainability and Uncertainty

Solving the Location Selection Problem of Self‐Service Stores from the Perspectives of Sustainability and Uncertainty

Characterization modeling of wake vortex circulation decay under reasonable worst case conditions with cuckoo search algorithm

Environmental Assessment of a Diesel Engine Fueled with Various Biodiesel Blends: Polynomial Regression and Grey Wolf Optimization

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