Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study

Mostafaeipour, Ali; Goli, Alireza; Qolipour, Mojtaba

doi:10.1007/s11227-018-2452-0

Cited by 41 publications

(13 citation statements)

References 27 publications

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“…Shuffled frog-leaping algorithm (SFLA) is a metaheuristic optimization algorithm inspired by the social behavior of frogs (Eusuff and Lansey, 2006; Goli et al, 2018). In terms of classification, SFLA falls into the category of behavioral algorithms or memetic algorithms (Mostafaeipour et al, 2018). SFLA is a developed version of the shuffled complex evolution algorithm (SCE or SCE-UA), which is one of the fairly old algorithms in the field of intelligent optimization.…”

Section: Methodsmentioning

confidence: 99%

Performance of different hybrid algorithms for prediction of wind speed behavior

et al. 2019

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This study seeks to provide a new method by proposing three hybrid algorithms. The proposed algorithms include genetic neural network hybrid algorithm, simulated annealing neural network hybrid algorithm, and shuffled frog-leaping neural network hybrid algorithm. The efficiency and reliability of the presented hybrid algorithms in prediction of wind speed behavior were evaluated using meteorological data of the city of Abadeh for a 10-year period from 2005 to 2015. The forecasting horizon is monthly for this study. The study parameters consisted of TMAX, TMIN, VP, RHMIN, RHMAX, WIND SPEED, PRECIPITATION, and SUNSHINE HOURS. These eight parameters are used as the inputs, and one parameter (ET) is used as the output. Research findings show that the shuffled frog-leaping neural network hybrid algorithm providing a root mean square error value of 0.0761 and reliability of 0.91 is more suitable than other hybrid algorithms for prediction of wind speed behavior in the study area.

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Section: Methodsmentioning

confidence: 99%

Performance of different hybrid algorithms for prediction of wind speed behavior

et al. 2019

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“…There are various methods present in the literature to tackle these problems. Mostafaeipour et al [184] proposed a meta‐heuristic method such as bat and firefly algorithm to calculate travel demand of a transportation network. A spatiotemporal fuzzy neural network for passenger demand is proposed by Liang et al [185].…”

Section: Future Of Crowd Intelligence In Transportation Systemmentioning

confidence: 99%

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

Shit

2020

IET Intelligent Transport Systems

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“…In this regard, the models' performance should be evaluated via criteria based on statistical errors. In practice, as it is possible two or more models be equivalent in most respects, we used various criteria such as mean square error (MSE) and its square root (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and median absolute percentage error (MDAPE) and the correlation coefficient (R 2 ) described by the following expressions (Mostafaeipour et al, 2018):…”

Section: Criteria For Comparing the Performance Of Modelsmentioning

confidence: 99%

Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence

Jafarian‐Namin

Goli

Qolipour

et al. 2019

IJESM

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Purpose The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria. Design/methodology/approach The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months. Findings The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480. Originality/value Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

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Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study

Cited by 41 publications

References 27 publications

Performance of different hybrid algorithms for prediction of wind speed behavior

Performance of different hybrid algorithms for prediction of wind speed behavior

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence

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