Hassan Rabie - Academia.edu (original) (raw)

Papers by Hassan Rabie

Abstract-In designing neural networks for time series forecasting, the number of input nodes is p... more Abstract-In designing neural networks for time series forecasting, the number of input nodes is perhaps the most important factor. The simple and commonly used method is to try different combinations of input variables and choose the one giving the minimum ...

International Journal of Advanced Computer Science and Applications

In this paper, a new hybridization of a Myopic and Neighborhood approaches is proposed to solve l... more In this paper, a new hybridization of a Myopic and Neighborhood approaches is proposed to solve large-size vertex p-median location problems. The effectiveness and efficiency of our approach are demonstrated empirically through an intensive computational experiment on large-size instances taken from TSPLib and BIRCH datasets, with the number of nodes varying from 734 to 9,976 for the former and from 9,600 to 20,000 nodes for the latter. The results show that the new approach, though relatively simple, yields better solutions compared to the ones in the literature. This demonstrates that a simpler approach that takes into account the advantages of other methods can lead to promising outcome and has the potential of being adopted in other combinatorial optimization problems.

International Journal of Advanced Computer Science and Applications, 2022

In this paper, a new hybridization of a Myopic and Neighborhood approaches is proposed to solve l... more In this paper, a new hybridization of a Myopic and Neighborhood approaches is proposed to solve large-size vertex p-median location problems. The effectiveness and efficiency of our approach are demonstrated empirically through an intensive computational experiment on large-size instances taken from TSPLib and BIRCH datasets, with the number of nodes varying from 734 to 9,976 for the former and from 9,600 to 20,000 nodes for the latter. The results show that the new approach, though relatively simple, yields better solutions compared to the ones in the literature. This demonstrates that a simpler approach that takes into account the advantages of other methods can lead to promising outcome and has the potential of being adopted in other combinatorial optimization problems.

Locating facilities on anywhere on a network is known as the absolute p-center location problem a... more Locating facilities on anywhere on a network is known as the absolute p-center location problem and it is proven to be NP-hard problem. Most of the recent approaches solve large-scale vertex p-center location problem in which facilities can be located only on the nodes of the network. However, rarely algorithms are developed to solve large-scale absolute p-center problem. Particle swarm optimization (PSO) is a metaheuristic approach, which recently proved to be a successful approach in solving complex continuous optimization problems. In this paper we present a PSO algorithm for the absolute p-center problem to minimize the maximum distance from each customer to his/her nearest facility. We have tested our proposed algorithm on a set of 12 problems from “Beasley OR Library ” and compared the results of vertex location problem to those of the absolute location problem. The numerical experiments show that PSO algorithm can solve optimally large-scale location problems with networks up...

neural-forecasting-competition.com

2014 10th International Computer Engineering Conference (ICENCO), 2014

In designing neural networks for time series forecasting, the number of input nodes is perhaps th... more In designing neural networks for time series forecasting, the number of input nodes is perhaps the most important factor. The simple and commonly used method is to try different combinations of input variables and choose the one giving the minimum forecasting error. Therefore, to select a smaller subset of inputs from a complete set is a combinational problem, and the selection process can be quite time consuming. In this paper we explore a methodological approach to input selection for time series forecasting. We compare it with some of the more heuristic/ad-hoc approaches and use the Cairo and Alexandria Stock Exchange (CASE30) Egyptian stock market data set for evaluating the merit of different approaches. The results presented here further strengthens the claim regarding the veracity of methodological input selection for time series forecasting.

Abstract-In designing neural networks for time series forecasting, the number of input nodes is p... more Abstract-In designing neural networks for time series forecasting, the number of input nodes is perhaps the most important factor. The simple and commonly used method is to try different combinations of input variables and choose the one giving the minimum ...

International Journal of Advanced Computer Science and Applications

In this paper, a new hybridization of a Myopic and Neighborhood approaches is proposed to solve l... more In this paper, a new hybridization of a Myopic and Neighborhood approaches is proposed to solve large-size vertex p-median location problems. The effectiveness and efficiency of our approach are demonstrated empirically through an intensive computational experiment on large-size instances taken from TSPLib and BIRCH datasets, with the number of nodes varying from 734 to 9,976 for the former and from 9,600 to 20,000 nodes for the latter. The results show that the new approach, though relatively simple, yields better solutions compared to the ones in the literature. This demonstrates that a simpler approach that takes into account the advantages of other methods can lead to promising outcome and has the potential of being adopted in other combinatorial optimization problems.

International Journal of Advanced Computer Science and Applications, 2022

In this paper, a new hybridization of a Myopic and Neighborhood approaches is proposed to solve l... more In this paper, a new hybridization of a Myopic and Neighborhood approaches is proposed to solve large-size vertex p-median location problems. The effectiveness and efficiency of our approach are demonstrated empirically through an intensive computational experiment on large-size instances taken from TSPLib and BIRCH datasets, with the number of nodes varying from 734 to 9,976 for the former and from 9,600 to 20,000 nodes for the latter. The results show that the new approach, though relatively simple, yields better solutions compared to the ones in the literature. This demonstrates that a simpler approach that takes into account the advantages of other methods can lead to promising outcome and has the potential of being adopted in other combinatorial optimization problems.

Locating facilities on anywhere on a network is known as the absolute p-center location problem a... more Locating facilities on anywhere on a network is known as the absolute p-center location problem and it is proven to be NP-hard problem. Most of the recent approaches solve large-scale vertex p-center location problem in which facilities can be located only on the nodes of the network. However, rarely algorithms are developed to solve large-scale absolute p-center problem. Particle swarm optimization (PSO) is a metaheuristic approach, which recently proved to be a successful approach in solving complex continuous optimization problems. In this paper we present a PSO algorithm for the absolute p-center problem to minimize the maximum distance from each customer to his/her nearest facility. We have tested our proposed algorithm on a set of 12 problems from “Beasley OR Library ” and compared the results of vertex location problem to those of the absolute location problem. The numerical experiments show that PSO algorithm can solve optimally large-scale location problems with networks up...

neural-forecasting-competition.com

2014 10th International Computer Engineering Conference (ICENCO), 2014

In designing neural networks for time series forecasting, the number of input nodes is perhaps th... more In designing neural networks for time series forecasting, the number of input nodes is perhaps the most important factor. The simple and commonly used method is to try different combinations of input variables and choose the one giving the minimum forecasting error. Therefore, to select a smaller subset of inputs from a complete set is a combinational problem, and the selection process can be quite time consuming. In this paper we explore a methodological approach to input selection for time series forecasting. We compare it with some of the more heuristic/ad-hoc approaches and use the Cairo and Alexandria Stock Exchange (CASE30) Egyptian stock market data set for evaluating the merit of different approaches. The results presented here further strengthens the claim regarding the veracity of methodological input selection for time series forecasting.