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Abdullah Khan

Bio: Abdullah Khan is an academic researcher from Universiti Tun Hussein Onn Malaysia. The author has contributed to research in topics: Cuckoo search & Backpropagation. The author has an hindex of 11, co-authored 33 publications receiving 443 citations.

Papers
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Book ChapterDOI
24 Jun 2013
TL;DR: The simulation results show that the computational efficiency of BP training process is highly enhanced when coupled with the proposed hybrid method and the performance of the proposed Cuckoo Search Back-Propagation (CSBP) is compared with artificial bee colony using BP algorithm, and other hybrid variants.
Abstract: Back-propagation Neural Network (BPNN) algorithm is one of the most widely used and a popular technique to optimize the feed forward neural network training. Traditional BP algorithm has some drawbacks, such as getting stuck easily in local minima and slow speed of convergence. Nature inspired meta-heuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposed a new meta-heuristic search algorithm, called cuckoo search (CS), based on cuckoo bird's behavior to train BP in achieving fast convergence rate and to avoid local minima problem. The performance of the proposed Cuckoo Search Back-Propagation (CSBP) is compared with artificial bee colony using BP algorithm, and other hybrid variants. Specifically OR and XOR datasets are used. The simulation results show that the computational efficiency of BP training process is highly enhanced when coupled with the proposed hybrid method.

85 citations

Journal ArticleDOI
TL;DR: An improved Levenberg Marquardt (LM) based back propagation (BP) trained with Cuckoo search algorithm for fast and improved convergence speed of the hybrid neural networks learning method.

84 citations

Journal ArticleDOI
01 Nov 2016
TL;DR: A new flower pollination algorithm with remarkable balance between consistency and exploration for NN training to build a model for the forecasting of petroleum consumption by the Organization of the Petroleum Exporting Countries (OPEC).
Abstract: Display Omitted We proposed a new forecasting method based on mete-heuristic algorithm.The method was applied to forecast OPEC petroleum consumption.The new method outperforms previous methods in forecasting OPEC petroleum consumption.The new method is an alternative means of forecasting OPEC petroleum consumption. Petroleum is the live wire of modern technology and its operations, with economic development being positively linked to petroleum consumption. Many meta-heuristic algorithms have been proposed in literature for the optimization of Neural Network (NN) to build a forecasting model. In this paper, as an alternative to previous methods, we propose a new flower pollination algorithm with remarkable balance between consistency and exploration for NN training to build a model for the forecasting of petroleum consumption by the Organization of the Petroleum Exporting Countries (OPEC). The proposed approach is compared with established meta-heuristic algorithms. The results show that the new proposed method outperforms existing algorithms by advancing OPEC petroleum consumption forecast accuracy and convergence speed. Our proposed method has the potential to be used as an important tool in forecasting OPEC petroleum consumption to be used by OPEC authorities and other global oil-related organizations. This will facilitate proper monitoring and control of OPEC petroleum consumption.

52 citations

Book ChapterDOI
01 Jan 2014
TL;DR: This paper investigates the use of Bat algorithm in combination with Back-propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory and to increase the convergence rate.
Abstract: Metaheuristic algorithm such as BAT algorithm is becoming a popular method in solving many hard optimization problems. This paper investigates the use of Bat algorithm in combination with Back-propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory and to increase the convergence rate. The performance of the proposed Bat based Back-Propagation (Bat-BP) algorithm is compared with Artificial Bee Colony using BPNN algorithm (ABC-BP) and simple BPNN algorithm. Specifically, OR and XOR datasets are used for training the network. The simulation results show that the computational efficiency of BPNN training process is highly enhanced when combined with BAT algorithm.

41 citations

Journal ArticleDOI
TL;DR: A new metaheuristic search algorithm based on Cuckoo bird’s behavior to train Elman recurrent network (ERN) and back propagation Elman recurring network (BPERN) in achieving fast convergence rate and to avoid local minima problem is proposed.
Abstract: Recurrent neural network (RNN) has been widely used as a tool in the data classification. This network can be educated with gradient descent back propagation. However, traditional training algorithms have some drawbacks such as slow speed of convergence being not definite to find the global minimum of the error function since gradient descent may get stuck in local minima. As a solution, nature inspired metaheuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposes a new metaheuristic search algorithm called Cuckoo Search (CS) based on Cuckoo bird’s behavior to train Elman recurrent network (ERN) and back propagation Elman recurrent network (BPERN) in achieving fast convergence rate and to avoid local minima problem. The proposed CSERN and CSBPERN algorithms are compared with artificial bee colony using BP algorithm and other hybrid variants algorithms. Specifically, some selected benchmark classification problems are used. The simulation results show that the computational efficiency of ERN and BPERN training process is highly enhanced when coupled with the proposed hybrid method.

40 citations


Cited by
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Book
01 Jan 1994

607 citations

Journal ArticleDOI
TL;DR: Particle swarm optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms as discussed by the authors.
Abstract: Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

260 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a taxonomy of CNN acceleration methods in terms of three levels, i.e. structure level, algorithm level, and implementation level, for CNN architectures compression, algorithm optimization and hardware-based improvement.

233 citations

Journal ArticleDOI
TL;DR: A comprehensive review of all conducting intensive research survey into the pros and cons, main architecture, and extended versions of this algorithm.

216 citations