This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational... more This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational Search Algorithm (DGSA) for discrete optimization problems. In DGSA, an agent's position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions are used to evaluate the performance of the proposed DGSA. The experimental result shows that the FDGSA able to find better solutions and converges faster compared to the Binary Gravitational Search Algorithm.
This paper presents the use of meta-heuristic technique to obtain three parameters (KP, KI and KD... more This paper presents the use of meta-heuristic technique to obtain three parameters (KP, KI and KD) of PID controller for Coupled Tank System (CTS). Particle Swarm Optimization (PSO) is chosen and Sum Squared Error is selected as objective function. This PSO is implemented for controlling desired liquid level of CTS. Then, the performances of the system are compared to various conventional techniques which are Trial and Error, Auto-Tuning, Ziegler-Nichols (Z-N) and Cohen-Coon (C-C) method. Simulation is conducted within Matlab environment to verify the transient response specifications in terms of Rise Time (TR), Settling Time (TS), Steady State Error (SSE) and Overshoot (OS). Result obtained shows that performance of CTS can be improved via PSO as PID tuning methods.
This paper reports the finding of the experimentation of the Particle Swarm Optimization in opti... more This paper reports the finding of the experimentation of the Particle Swarm Optimization in optimizing the stereo matching algorithm’s parameters for the star fruit inspection system. The star fruit inspection system is built by CvviP Universiti Teknologi Malaysia. While the stereo matching algorithm used in the experiment is taken from the Matlab library. Each particle of Particle Swarm Optimization in the search pace repsents a set of candidate numerical value of the stereo matching’s parameters. The fitness function for this application is the sum of absolute error of the gray scale value of both images. Based on this information, the particles will improve its position in the search space by moving towards its best record and the swarm best record. The process repeated until the maximum iteration met. The result indicates that there is potential application of Particle Swarm Optimization in stereo matching’s parameters tuning.
Woods species recognition is a texture classification difficulty that has been studied by many re... more Woods species recognition is a texture classification difficulty that has been studied by many researchers years ago. The species of the wood are identified by the proposed classification using the textural type that can be observed on the structural features for example the colour of the woods, weight, texture and other features. Any mistakes on texture recognition will affect the economic benefits of wood production where it is an important basis for an identification of woods. Besides, to guide a person to be skilled in wood recognition, it will take a long time and the result the wood sample can be biased. These kinds of problem can be a motivation to develop a system that can recognize the wood effectively. This project will try to integrate both attempts by proposing a classification system consists of feature extractor, classifier and optimizer. The project proposes a classification system using Gray Level Co-Occurrence Matrix (GLCM) as feature extractor, K-Nearest Neighbor (K-NN) as classifier and Binary Gravitational Search Algorithm (BGSA) as the optimizer for GLCM's feature selection and parameters. For this project, images of wood knot from CAIRO UTM database are used for benchmarking the proposed system performance. The result shows that the proposed approach can perform as good as previous literatures with fewer features used as input for the classifier.
Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic ... more Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorithm. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm.
Previously, route planning in holes drilling process has been taken for granted due to its automa... more Previously, route planning in holes drilling process has been taken for granted due to its automated process, in nature. But as the interest to make Computer Numerical Control machines more efficient, there have been a steady increase in number of studies for the past decade. Many researchers proposed algorithms that belong into Computational Intelligence, due to their simplicity and ability to obtain optimal result. In this study, an optimization algorithm based on Gravitational Search Algorithm is proposed for solving route optimization in holes drilling process. The proposed approach involves modeling and simulation of Gravitational Search Algorithm. The performance of the algorithm is benchmark with one case study that had been frequently used by previous researchers. The result indicates that the proposed approach performs better than most of the literatures.
This paper presents development of an optimal PID and PD controllers for controlling the nonlinea... more This paper presents development of an optimal PID and PD controllers for controlling the nonlinear Gantry Crane System (GCS). A new method of Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is developed to obtain optimal PID and PD parameters. The optimal parameters are tested on the control structure to examine system responses including trolley displacement and payload oscillation. The dynamic model of GCS is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of the system in terms of settling time, steady state error and overshoot. The result not only confirmed the successes of using new method for GCS, but also shows the new method performs more efficiently compared to the continuous PSO. This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the desired position with low payload oscillation.
Industrial tank system is widely used in consumer liquid processing and chemical processing indus... more Industrial tank system is widely used in consumer liquid processing and chemical processing industry. In liquid-based product manufacturing system, one of the main components consists of an industrial tank. This paper explores the applications of two swarm intelligence algorithms in optimizing the PID controller parameters. These swarm intelligence algorithms are Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). Each agent of the swarm intelligence will represent a possible solution of the problem where each dimension corresponds to the PID controller’s parameters. Result obtained shows that there are potential in improving these algorithms to replace the conventional way of obtaining PID controller’s parameters.
The price of the wood according to the type of wood. Classification of the woods can be done by s... more The price of the wood according to the type of wood. Classification of the woods can be done by studying its texture. This paper introduces Fuzzy k Nearest Neighbor to classify 25 types of wood. The wood’s images have been taken from the Wood Database of the Centre for Artificial Intelligence & Robotics, Universiti Teknologi Malaysia. The features of wood images are extracted using Local Binary Pattern. The results of this paper shows improvement in wood classification compare to the previous literature.
This paper presents development of an optimal PID and PD controllers for controlling the nonlinea... more This paper presents development of an optimal PID and PD controllers for controlling the nonlinear gantry crane system. The proposed Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is adopted in obtaining five optimal controller gains. The optimal gains are tested on a control structure that combines PID and PD controllers to examine system responses including trolley displacement and payload oscillation. The dynamic model of gantry crane system is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the various desired position.
The purpose of this paper was to design a much simpler control method for a wastewater treatment ... more The purpose of this paper was to design a much simpler control method for a wastewater treatment plant. The work proposes a direct adaptive predictive control (DAMPC) also known as subspace predictive control (SPC) as a solution to the conventional one. The adaptive control structure is based on the linear model of the process and combined with numerical algorithm for subspace state space system identification (N4SID). This N4SID plays the role of the software sensor for on-line estimation of prediction matrices and control matrices of the bioprocess, joint together with model predictive control (MPC) in order to obtain the optimal control sequence. The performances of both estimation and control algorithms are illustrated by simulation results. Stability analysis is done to investigate the response of the system proposed when parameter changes exist. This project proves that subspace-adaptive method has a large number of important and useful advantages, primarily the application ability to Multi Input Multi Output (MIMO) systems, and the low requirements on prior system information. Given the advantages observed, the most likely areas of application for the proposed algorithm are multivariable processes, about which little information is known such as this wastewater treatment plant. Hence, direct adaptive predictive control (DAMPC) can provide simplicity and good performance in of an activated sludge process.
System modeling is very important to develop a mathematical model that describes the dynamics of... more System modeling is very important to develop a mathematical model that describes the dynamics of a system. This work proposes a modeling and designing a Generalized Minimum Variance (GMV) controller using self-tuning and Particle Swarm Optimization (PSO) tuning method for a hot air blower system. Parametric approach using AutoRegressive with Exogenous input (ARX) model structure is used to estimate the approximated model plant. The approximated plant model is then being estimated using System Identification approach. The results based on simulation using MATLAB shows that the GMV controller using PSO tuning method offers a reasonable tracking performances of the system's output.
The use of ambulance location model is significant in determining the best ambulance locations to... more The use of ambulance location model is significant in determining the best ambulance locations to ensure efficient emergency medical services (EMS) delivery. Maximal Covering Location Problem (MCLP) is one of the most common location models. It is an NP-hard problem and the objective is to maximize the coverage by a fixed number of ambulances. In this study, the demand zones are distributed in a grid based hypothetical region and each zone can host at most one ambulance only. The effectiveness of using Particle Swarm Optimization (PSO) algorithm in finding the best solution for MCLP problem is investigated. The result is compared with the random search technique. It was found that the proposed method manages to identify global optimal solution at a reasonable search time.
This paper presents development of an optimal PID and PD controllers for controlling the nonlinea... more This paper presents development of an optimal PID and PD controllers for controlling the nonlinear gantry crane system. The proposed Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is adopted in obtaining five optimal controller gains. The optimal gains are tested on a control structure that combines PID and PD controllers to examine system responses including trolley displacement and payload oscillation. The dynamic model of gantry crane system is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the various desired position.
Travelling Salesman Problem is a mathematical problem that describes a salesman problem in findin... more Travelling Salesman Problem is a mathematical problem that describes a salesman problem in finding the shortest distance to travel to all the cities given that each city is visited once only. Travelling Salesman Problem is nondeterministic and a polynomial time problem, which makes the conventional mathematical optimization techniques, irrelevant to solve the problem. This paper explores the use of one of swarm intelligence available, Firefly Algorithm in finding the optimal solution of the problem. Each firefly in Firefly Algorithm represents a candidate solution of the Travelling Salesman Problem. The candidate solution is modeled using a voting technique where each dimension of the firefly in search space represents a city that need to be visited by the salesman. The city with largest vote will be the initial city of the salesman, while the city with least vote, will be the second last city visited by the salesman before going back to the first city, he comes from. Each of this candidate solution has a distance that correlates with the fitness value of the firefly. A firefly will try to improve the solution its represents by moving closer to other fireflies with better fitness values, in the search space. This process is repeated until a stopping condition reached. The performance of the proposed approach is benchmark with a case study.
The main objective in camera auto calibration is to find intrinsic parameters values that minimiz... more The main objective in camera auto calibration is to find intrinsic parameters values that minimize the cost function. This paper attempts to implement a stochastic optimization algorithm called Bat Algorithm in order to find optimal values of the intrinsic parameters. Each bat in Bat Algorithm represents a candidate solution for the problem and each dimension in the search space of the Bat Algorithm represents a parameter of intrinsic parameters: skew, focal length, and magnification factor. The cost function used in this paper is based on the Kruppa’s equation. For each iteration, the bats will try to improve its fitness by following the echolocation behavior of the microbats. A case study taken from database, provided by Le2i Universite de Bourgoune is used to evaluate the performance of the Bat Algorithm. The result obtained indicates potential application with further improvement required.
This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational... more This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational Search Algorithm (DGSA) for discrete optimization problems. In DGSA, an agent's position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions are used to evaluate the performance of the proposed DGSA. The experimental result shows that the FDGSA able to find better solutions and converges faster compared to the Binary Gravitational Search Algorithm.
This paper presents the use of meta-heuristic technique to obtain three parameters (KP, KI and KD... more This paper presents the use of meta-heuristic technique to obtain three parameters (KP, KI and KD) of PID controller for Coupled Tank System (CTS). Particle Swarm Optimization (PSO) is chosen and Sum Squared Error is selected as objective function. This PSO is implemented for controlling desired liquid level of CTS. Then, the performances of the system are compared to various conventional techniques which are Trial and Error, Auto-Tuning, Ziegler-Nichols (Z-N) and Cohen-Coon (C-C) method. Simulation is conducted within Matlab environment to verify the transient response specifications in terms of Rise Time (TR), Settling Time (TS), Steady State Error (SSE) and Overshoot (OS). Result obtained shows that performance of CTS can be improved via PSO as PID tuning methods.
This paper reports the finding of the experimentation of the Particle Swarm Optimization in opti... more This paper reports the finding of the experimentation of the Particle Swarm Optimization in optimizing the stereo matching algorithm’s parameters for the star fruit inspection system. The star fruit inspection system is built by CvviP Universiti Teknologi Malaysia. While the stereo matching algorithm used in the experiment is taken from the Matlab library. Each particle of Particle Swarm Optimization in the search pace repsents a set of candidate numerical value of the stereo matching’s parameters. The fitness function for this application is the sum of absolute error of the gray scale value of both images. Based on this information, the particles will improve its position in the search space by moving towards its best record and the swarm best record. The process repeated until the maximum iteration met. The result indicates that there is potential application of Particle Swarm Optimization in stereo matching’s parameters tuning.
Woods species recognition is a texture classification difficulty that has been studied by many re... more Woods species recognition is a texture classification difficulty that has been studied by many researchers years ago. The species of the wood are identified by the proposed classification using the textural type that can be observed on the structural features for example the colour of the woods, weight, texture and other features. Any mistakes on texture recognition will affect the economic benefits of wood production where it is an important basis for an identification of woods. Besides, to guide a person to be skilled in wood recognition, it will take a long time and the result the wood sample can be biased. These kinds of problem can be a motivation to develop a system that can recognize the wood effectively. This project will try to integrate both attempts by proposing a classification system consists of feature extractor, classifier and optimizer. The project proposes a classification system using Gray Level Co-Occurrence Matrix (GLCM) as feature extractor, K-Nearest Neighbor (K-NN) as classifier and Binary Gravitational Search Algorithm (BGSA) as the optimizer for GLCM's feature selection and parameters. For this project, images of wood knot from CAIRO UTM database are used for benchmarking the proposed system performance. The result shows that the proposed approach can perform as good as previous literatures with fewer features used as input for the classifier.
Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic ... more Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorithm. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm.
Previously, route planning in holes drilling process has been taken for granted due to its automa... more Previously, route planning in holes drilling process has been taken for granted due to its automated process, in nature. But as the interest to make Computer Numerical Control machines more efficient, there have been a steady increase in number of studies for the past decade. Many researchers proposed algorithms that belong into Computational Intelligence, due to their simplicity and ability to obtain optimal result. In this study, an optimization algorithm based on Gravitational Search Algorithm is proposed for solving route optimization in holes drilling process. The proposed approach involves modeling and simulation of Gravitational Search Algorithm. The performance of the algorithm is benchmark with one case study that had been frequently used by previous researchers. The result indicates that the proposed approach performs better than most of the literatures.
This paper presents development of an optimal PID and PD controllers for controlling the nonlinea... more This paper presents development of an optimal PID and PD controllers for controlling the nonlinear Gantry Crane System (GCS). A new method of Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is developed to obtain optimal PID and PD parameters. The optimal parameters are tested on the control structure to examine system responses including trolley displacement and payload oscillation. The dynamic model of GCS is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of the system in terms of settling time, steady state error and overshoot. The result not only confirmed the successes of using new method for GCS, but also shows the new method performs more efficiently compared to the continuous PSO. This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the desired position with low payload oscillation.
Industrial tank system is widely used in consumer liquid processing and chemical processing indus... more Industrial tank system is widely used in consumer liquid processing and chemical processing industry. In liquid-based product manufacturing system, one of the main components consists of an industrial tank. This paper explores the applications of two swarm intelligence algorithms in optimizing the PID controller parameters. These swarm intelligence algorithms are Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). Each agent of the swarm intelligence will represent a possible solution of the problem where each dimension corresponds to the PID controller’s parameters. Result obtained shows that there are potential in improving these algorithms to replace the conventional way of obtaining PID controller’s parameters.
The price of the wood according to the type of wood. Classification of the woods can be done by s... more The price of the wood according to the type of wood. Classification of the woods can be done by studying its texture. This paper introduces Fuzzy k Nearest Neighbor to classify 25 types of wood. The wood’s images have been taken from the Wood Database of the Centre for Artificial Intelligence & Robotics, Universiti Teknologi Malaysia. The features of wood images are extracted using Local Binary Pattern. The results of this paper shows improvement in wood classification compare to the previous literature.
This paper presents development of an optimal PID and PD controllers for controlling the nonlinea... more This paper presents development of an optimal PID and PD controllers for controlling the nonlinear gantry crane system. The proposed Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is adopted in obtaining five optimal controller gains. The optimal gains are tested on a control structure that combines PID and PD controllers to examine system responses including trolley displacement and payload oscillation. The dynamic model of gantry crane system is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the various desired position.
The purpose of this paper was to design a much simpler control method for a wastewater treatment ... more The purpose of this paper was to design a much simpler control method for a wastewater treatment plant. The work proposes a direct adaptive predictive control (DAMPC) also known as subspace predictive control (SPC) as a solution to the conventional one. The adaptive control structure is based on the linear model of the process and combined with numerical algorithm for subspace state space system identification (N4SID). This N4SID plays the role of the software sensor for on-line estimation of prediction matrices and control matrices of the bioprocess, joint together with model predictive control (MPC) in order to obtain the optimal control sequence. The performances of both estimation and control algorithms are illustrated by simulation results. Stability analysis is done to investigate the response of the system proposed when parameter changes exist. This project proves that subspace-adaptive method has a large number of important and useful advantages, primarily the application ability to Multi Input Multi Output (MIMO) systems, and the low requirements on prior system information. Given the advantages observed, the most likely areas of application for the proposed algorithm are multivariable processes, about which little information is known such as this wastewater treatment plant. Hence, direct adaptive predictive control (DAMPC) can provide simplicity and good performance in of an activated sludge process.
System modeling is very important to develop a mathematical model that describes the dynamics of... more System modeling is very important to develop a mathematical model that describes the dynamics of a system. This work proposes a modeling and designing a Generalized Minimum Variance (GMV) controller using self-tuning and Particle Swarm Optimization (PSO) tuning method for a hot air blower system. Parametric approach using AutoRegressive with Exogenous input (ARX) model structure is used to estimate the approximated model plant. The approximated plant model is then being estimated using System Identification approach. The results based on simulation using MATLAB shows that the GMV controller using PSO tuning method offers a reasonable tracking performances of the system's output.
The use of ambulance location model is significant in determining the best ambulance locations to... more The use of ambulance location model is significant in determining the best ambulance locations to ensure efficient emergency medical services (EMS) delivery. Maximal Covering Location Problem (MCLP) is one of the most common location models. It is an NP-hard problem and the objective is to maximize the coverage by a fixed number of ambulances. In this study, the demand zones are distributed in a grid based hypothetical region and each zone can host at most one ambulance only. The effectiveness of using Particle Swarm Optimization (PSO) algorithm in finding the best solution for MCLP problem is investigated. The result is compared with the random search technique. It was found that the proposed method manages to identify global optimal solution at a reasonable search time.
This paper presents development of an optimal PID and PD controllers for controlling the nonlinea... more This paper presents development of an optimal PID and PD controllers for controlling the nonlinear gantry crane system. The proposed Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is adopted in obtaining five optimal controller gains. The optimal gains are tested on a control structure that combines PID and PD controllers to examine system responses including trolley displacement and payload oscillation. The dynamic model of gantry crane system is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the various desired position.
Travelling Salesman Problem is a mathematical problem that describes a salesman problem in findin... more Travelling Salesman Problem is a mathematical problem that describes a salesman problem in finding the shortest distance to travel to all the cities given that each city is visited once only. Travelling Salesman Problem is nondeterministic and a polynomial time problem, which makes the conventional mathematical optimization techniques, irrelevant to solve the problem. This paper explores the use of one of swarm intelligence available, Firefly Algorithm in finding the optimal solution of the problem. Each firefly in Firefly Algorithm represents a candidate solution of the Travelling Salesman Problem. The candidate solution is modeled using a voting technique where each dimension of the firefly in search space represents a city that need to be visited by the salesman. The city with largest vote will be the initial city of the salesman, while the city with least vote, will be the second last city visited by the salesman before going back to the first city, he comes from. Each of this candidate solution has a distance that correlates with the fitness value of the firefly. A firefly will try to improve the solution its represents by moving closer to other fireflies with better fitness values, in the search space. This process is repeated until a stopping condition reached. The performance of the proposed approach is benchmark with a case study.
The main objective in camera auto calibration is to find intrinsic parameters values that minimiz... more The main objective in camera auto calibration is to find intrinsic parameters values that minimize the cost function. This paper attempts to implement a stochastic optimization algorithm called Bat Algorithm in order to find optimal values of the intrinsic parameters. Each bat in Bat Algorithm represents a candidate solution for the problem and each dimension in the search space of the Bat Algorithm represents a parameter of intrinsic parameters: skew, focal length, and magnification factor. The cost function used in this paper is based on the Kruppa’s equation. For each iteration, the bats will try to improve its fitness by following the echolocation behavior of the microbats. A case study taken from database, provided by Le2i Universite de Bourgoune is used to evaluate the performance of the Bat Algorithm. The result obtained indicates potential application with further improvement required.
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Papers by Amar Faiz Zainal Abidin