Air Passenger Demand Forecasting Using Particle Swarm Optimization and Firefly Algorithm
Air travel demand is a crucial part of planning for airlines and airports. It helps in elaborating decisions and recognizing risks and opportunities. Forecasting air passenger demand is an interesting research study that deserves investigation. This problem requires prediction techniques
such that Linear Regression and Neural Network. These techniques are efficient, but they have several parameters that necessitate appropriate values to provide the least error rate of prediction. Some recent air travel demand studies investigated Genetic Algorithms to provide optimal values
for these parameters. In this article, we propose to explore the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) to find the optimal values for Linear Regression (LR) coefficients. This study presents two new hybrid prediction techniques (PSO based LR and FA based LR) to handle
airline demand forecasting, which researchers have not previously covered. The results of PSO based LR, FA-based LR and LR are compared to find the best model with the lowest prediction error rate. The results showed that PSO based LR achieved the best prediction results with a lower error
rate compared to FA based LR and LR alone. This study is performed using the data of Los Angeles International airport.
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Air Passenger Demand;
Particle Swarm Optimization
Document Type: Research Article
Computer Science Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, KSA
Information Systems Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, KSA
Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, KSA
September 1, 2019
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Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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