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Teo, Kenneth Tze Kin
- An Evolutionary Algorithm for Channel Assignment Problem in Wireless Mobile Networks
Abstract Views :163 |
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Authors
Affiliations
1 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY
1 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY
Source
ICTACT Journal on Communication Technology, Vol 3, No 4 (2012), Pagination: 613-618Abstract
The channel assignment problem in wireless mobile network is the assignment of appropriate frequency spectrum to incoming calls while maintaining a satisfactory level of electromagnetic compatibility (EMC) constraints. An effective channel assignment strategy is important due to the limited capacity of frequency spectrum in wireless mobile network. Most of the existing channel assignment strategies are based on deterministic methods. In this paper, an adaptive genetic algorithm (GA) based channel assignment strategy is introduced for resource management and to reduce the effect of EMC interferences. The most significant advantage of the proposed optimization method is its capability to handle both the reassignment of channels for existing calls as well as the allocation of channel to a new incoming call in an adaptive process to maximize the utility of the limited resources. It is capable to adapt the population size to the number of eligible channels for a particular cell upon new call arrivals to achieve reasonable convergence speed. The MATLAB simulation on a 49-cells network model for both uniform and nonuniform call traffic distributions showed that the proposed channel optimization method can always achieve a lower average new incoming call blocking probability compared to the deterministic based channel assignment strategy.Keywords
Evolutionary Optimization, Genetic Algorithm, Hybrid Channel Assignment, Wireless Mobile Network.- Fuzzy Logic Based Energy Efficient Protocol in Wireless Sensor Networks
Abstract Views :207 |
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Authors
Affiliations
1 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY
2 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY
1 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY
2 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY
Source
ICTACT Journal on Communication Technology, Vol 3, No 4 (2012), Pagination: 639-645Abstract
Wireless sensor networks (WSNs) have been vastly developed due to the advances in microelectromechanical systems (MEMS) using WSN to study and monitor the environments towards climates changes. In environmental monitoring, sensors are randomly deployed over the interest area to periodically sense the physical environments for a few months or even a year. Therefore, to prolong the network lifetime with limited battery capacity becomes a challenging issue. Low energy adaptive cluster hierarchical (LEACH) is the common clustering protocol that aim to reduce the energy consumption by rotating the heavy workload cluster heads (CHs). The CHs election in LEACH is based on probability model which will lead to inefficient in energy consumption due to least desired CHs location in the network. In WSNs, the CHs location can directly influence the network energy consumption and further affect the network lifetime. In this paper, factors which will affect the network lifetime will be presented and the demonstration of fuzzy logic based CH selection conducted in base station (BS) will also be carried out. To select suitable CHs that will prolong the network first node dies (FND) round and consistent throughput to the BS, energy level and distance to the BS are selected as fuzzy inputs.Keywords
Wireless Sensor Network, Cluster Head, First Node Dies, Fuzzy Logic.- Particle Filter Based Vehicle Tracking Approach with Improved Resampling Stage
Abstract Views :199 |
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Authors
Affiliations
1 Modelling, Simulation and Computing Laboratory, Material and Mineral Research Unit, School of Engineering and Information Technology, Universiti Malaysia, Sabah, MY
1 Modelling, Simulation and Computing Laboratory, Material and Mineral Research Unit, School of Engineering and Information Technology, Universiti Malaysia, Sabah, MY
Source
ICTACT Journal on Image and Video Processing, Vol 4, No 3 (2014), Pagination: 725-732Abstract
Optical sensors based vehicle tracking can be widely implemented in traffic surveillance and flow control. The vast development of video surveillance infrastructure in recent years has drawn the current research focus towards vehicle tracking using high-end and low cost optical sensors. However, tracking vehicles via such sensors could be challenging due to the high probability of changing vehicle appearance and illumination, besides the occlusion and overlapping incidents. Particle filter has been proven as an approach which can overcome nonlinear and non-Gaussian situations caused by cluttered background and occlusion incidents. Unfortunately, conventional particle filter approach encounters particle degeneracy especially during and after the occlusion. Particle filter with sampling important resampling (SIR) is an important step to overcome the drawback of particle filter, but SIR faced the problem of sample impoverishment when heavy particles are statistically selected many times. In this work, genetic algorithm has been proposed to be implemented in the particle filter resampling stage, where the estimated position can converge faster to hit the real position of target vehicle under various occlusion incidents. The experimental results show that the improved particle filter with genetic algorithm resampling method manages to increase the tracking accuracy and meanwhile reduce the particle sample size in the resampling stage.Keywords
Vehicle Tracking, Particle Filter, Genetic Algorithm, Resampling, Occlusion.- Automatic License Plate Localisation and Identification via Signature Analysis
Abstract Views :188 |
PDF Views:0
Authors
Affiliations
1 Modelling, Simulation and Computing Laboratory, Material and Mineral Research Unit, School of Engineering and Information Technology, Universiti Malaysia, Sabah, MY
1 Modelling, Simulation and Computing Laboratory, Material and Mineral Research Unit, School of Engineering and Information Technology, Universiti Malaysia, Sabah, MY
Source
ICTACT Journal on Image and Video Processing, Vol 4, No 3 (2014), Pagination: 754-761Abstract
A new algorithm for license plate localisation and identification is proposed on the basis of Signature analysis. Signature analysis has been used to locate license plate candidate and its properties can be further utilised in supporting and affirming the license plate character recognition. This paper presents Signature Analysis and the improved conventional Connected Component Analysis (CCA) to design an automatic license plate localisation and identification. A procedure called Euclidean Distance Transform is added to the conventional CCA in order to tackle the multiple bounding boxes that occurred. The developed algorithm, SAICCA achieved 92% successful rate, with 8% failed localisation rate due to the restrictions such as insufficient light level, clarity and license plate perceptual information. The processing time for a license plate localisation and recognition is a crucial criterion that needs to be concerned. Therefore, this paper has utilised several approaches to decrease the processing time to an optimal value. The results obtained show that the proposed system is capable to be implemented in both ideal and non-ideal environments.Keywords
Vehicle Localisation, Automatic License Plate Recognition, Signature Analysis, Adaptive Searching, Euclidean Distance Transform.- Modelling and Control of Partially Shaded Photovoltaic Arrays
Abstract Views :164 |
PDF Views:0
Authors
Affiliations
1 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY
1 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY
Source
ICTACT Journal on Soft Computing, Vol 3, No 2 (2013), Pagination: 459-466Abstract
The photovoltaic (PV) array controlled by Maximum Power Point Tracking (MPPT) method for optimum PV power generation, particularly when the PV array is under partially shaded condition is presented in this paper. The system modelling is carried out in MATLAB-SIMULINK where the PV array is formed by five series connected identical PV modules. Under uniform solar irradiance conditions, the PV module and the PV array present nonlinear P-V characteristic but the maximum power point (MPP) can be easily identified. However, when the PV array is under shaded conditions, the P-V characteristic becomes more complex with the present of multiple MPP. While the PV array operated at local MPP, the generated power is limited. Thus, the investigation on MPPT approach is carried out to maximize the PV generated power even when the PV array is under partially shaded conditions (PSC). Fuzzy logic is adopted into the conventional MPPT to form fuzzy logic based MPPT (FMPPT) for better performance. The developed MPPT and FMPPT are compared, particularly the performances on the transient response and the steady state response when the array is under various shaded conditions. FMPPT shows better performance where the simulation results demonstrate FMPPT is able to facilitate the PV array to reach the MPP faster while it helps the PV array to produce a more stable output power.Keywords
Photovoltaic, Partially Shaded Conditions, MPPT, Fuzzy Logic, Perturb and Observe.- Optimization of Urban Multi-Intersection Traffic Flow via Q-Learning
Abstract Views :170 |
PDF Views:0
Authors
Affiliations
1 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY
1 Modeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, MY
Source
ICTACT Journal on Soft Computing, Vol 3, No 2 (2013), Pagination: 485-491Abstract
Congestions of the traffic flow within the urban traffic network have been a challenging task for all the urban developers. Many approaches have been introduced into the current system to solve the traffic congestion problems. Reconfiguration of the traffic signal timing plan has been carried out through implementation of different techniques. However, dynamic characteristics of the traffic flow increase the difficulties towards the ultimate solutions. Thus, traffic congestions still remain as unsolvable problems to the current traffic control system. In this study, artificial intelligence method has been introduced in the traffic light system to alter the traffic signal timing plan to optimize the traffic flows. Q-learning algorithm in this study has enhanced the traffic light system with learning ability. The learning mechanism of Q-learning enables traffic light intersections to release itself from traffic congestions situation. Adjacent traffic light intersections will work independently and yet cooperate with each others to a common goal of ensuring the fluency of the traffic flows within the traffic network. The simulated results show that the Q-Learning algorithm is able to learn from the dynamic traffic flow and optimize the traffic flow accordingly.Keywords
Reinforcement Learning, Q-Learning, Traffic Networks, Traffic Signal Timing Plan Management, Multi-Agents Systems.- Sumo Enhancement for Vehicular Communication Development
Abstract Views :257 |
PDF Views:3
Authors
Affiliations
1 Simulation and Computing Laboratory, Universiti Malaysia Sabah, MY
1 Simulation and Computing Laboratory, Universiti Malaysia Sabah, MY
Source
ICTACT Journal on Communication Technology, Vol 8, No 4 (2017), Pagination: 1625-1632Abstract
It is normal that every family is having at least one vehicle at their home as vehicles have become a daily needs for all of us. However, this also leads to the increased of road accidents where major causes are related to human errors which can be prevented. To tackle with this problem, vehicular ad hoc network (VANET) is introduced with the aim to make vehicles intelligent. In order to study the algorithm in VANET, a mobility simulator is needed for simulation purpose. In this case, SUMO is proved to be a good simulation tool in generating VANET environment while MATLAB is good for algorithm development. Yet, to develop a good simulation platform, modification on SUMO files are necessary. This paper discusses on the procedures in creating a left-hand traffic (LHT) simulation file that is suitable to be used in Malaysia. LHT simulation is not easy to achieve as modification on the road connection and traffic light files are required. This paper also showed the results of the simulation after SUMO files modification. Apart from that, this paper also showed the simulation of VANET environment using SUMO and MATLAB through a third party interfacing named TraCI4Matlab, which allows communication between MATLAB and SUMO simulator.Keywords
Mobility Simulator, SUMO, MATLAB, SUMO Files Modification.References
- O. Olarte, “Human Error Accounts for 90% of Road Accidents”, Available: http://www.alertdriving.com/home/fleet-alert-magazine/international/human-error-accounts-90-road-accidents, Accessed on 2016.
- M.Y. Choong, R.K.Y. Chin, K.B. Yeo and K.T.K. Teo, “Trajectory Clustering for Behavioral Pattern Learning in Transportation Surveillance”, Proceedings of 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, pp. 119-123, 2014.
- M.Y. Choong, R.K.Y. Chin, K.B. Yeo and K.T.K. Teo, “Trajectory Pattern Mining via Clustering based on Similarity Function for Transportation Surveillance”, International Journal of Simulation: Systems, Science and Technology, Vol. 17, No. 34, pp. 191-197, 2016.
- M.Y. Choong, L. Angeline, R.K.Y. Chin, K.B. Yeo and K.T.K. Teo, “Vehicle Trajectory Clustering for Traffic Intersection Surveillance”, Proceedings of IEEE International Conference on Consumer Electronics, pp. 783-787, 2016.
- H.S.E. Chuo, M.K. Tan, B.L. Chua, R.K.Y. Chin and K.T.K. Teo, “Computation of Cell Transmission Model for Congestion and Recovery Traffic Flow”, Proceedings of IEEE International Conference on Consumer Electronics, pp. 667-671, 2016.
- K.T.K. Teo, K.B. Yeo, S.E. Tan, Z.W. Siew and K.G. Lim, “Design and Development of Portable Fuzzy Logic based Traffic Optimizer”, Proceedings of IEEE International Conference on Consumer Electronics, pp. 783-776, 2013.
- K.T.K. Teo, K.B. Yeo, Y.K. Chin, H.S.E. Chuo and M.K. Tan, “Agent-based Traffic Flow Optimization at Multiple Signalized Intersections”, Proceedings of 8th Asia International Conference on Mathematical Modelling and Computer Simulation, pp. 459-463, 2014.
- C.H. Lee, K.G. Lim, B.L. Chua, R.K.Y. Chin and K.T.K. Teo, “Progressing Toward Urban Topology and Mobility Trace for Vehicular Ad Hoc Network (VANET)”, Proceedings of IEEE Conference on Open Systems, pp. 343-346, 2016.
- V. Kumar, S. Mishra and N. Chand, “Applications of VANETs: Present and Future”, Computer Science and Communications, Vol. 5, No. 1, pp. 12-15, 2013.
- C.H. Lee, K.G. Lim, B.L. Chua, R.K.Y. Chin and K.T.K. Teo, “Performance Evaluation of IEEE 802.11 for Vehicular Communication”, Proceedings of IEEE International Conference on Consumer Electronics, pp. 743-747, 2016.
- C. Sommer, I. Dietrich and F. Dressler, “Realistic Simulation of Network Protocols in VANET Scenarios”, Proceedings of International Conference on Mobile Networking for Vehicular Environments, pp. 117-119, 2007.
- H. Hartenstein and K.P. Laberteaux, “A Tutorial Survey on Vehicular Ad Hoc Network”, IEEE Communications Magazine, Vol. 46, No. 6, pp. 164-171, 2008.
- M.K. Patel, “Comparative Study of Vehicular Ad-hoc Network Mobility Models and Simulators”, International Journal of Computer Applications, Vol. 47, No. 6, pp. 38-43, 2012.
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- K.G. Lim, C.H. Lee, R.K.Y. Chin, K.B. Yeo and K.T.K. Teo, “Simulators for Vehicular Ad Hoc Network (VANET) Development”, Proceedings of IEEE International Conference on Consumer Electronics, pp. 913-917, 2016.
- R.L. Bertini, R. Lindgren and S. Tantiyanugulchai, “Applications of Paramics Simulation at a Diamond Interchange”, Research Report, Portland State University, 2002.
- P.V. Martin Fellendorf, “Validation of the Microscopic Traffic Flow Model VISSIM in Different Real-World Situations”, Proceedings of Annual Meeting on Transportation Research, pp. 171-175, 2001.
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- T. MathWorks, Available at: http://www.mathworks.com/matlabcentral/fileexchange/41832-vanet-node-roadside-unit-scenario-simulations, Accessed on 2013.
- TraCI4Matlab: User's Manual, Available at: https://in.mathworks.com/matlabcentral/fileexchange/44805-traci4matlab?requestedDomain=www.mathworks.com
- M.K. Tan, H.S.E. Chuo, R.K.Y. Chin, K.B. Yeo and K.T.K. Teo, “Optimization of Urban Traffic Network Signalization using Genetic Algorithm”, Proceedings of IEEE Conference on Open Systems, pp. 943-946, 2016.
- M.K. Tan, H.S.E. Chuo, R.K.Y. Chin, K.B. Yeo and K.T.K. Teo, “Genetic Algorithm based Signal Optimizer for Oversaturated Urban Signalized Intersection”, Proceedings of IEEE International Conference on Consumer Electronics, pp. 945-949, 2016.
- K.T.K. Teo, R.K.Y. Chin, S.E. Tan, C.H. Lee and K.G. Lim, “Exploration of Genetic Algorithm in Network Coding for Wireless Sensor Networks”, International Journal of Simulation: Systems, Science and Technology, Vol. 15, No. 6, pp. 83-89, 2014.
- K.T.K. Teo, R.K.Y. Chin, S.E. Tan, C.H. Lee and K.G. Lim, “Performance Analysis of Enhanced Genetic Algorithm based Network Coding in Wireless Networks”, Proceedings of 8th Asia International Conference on Mathematical Modelling and Computer Simulation, pp. 49-53, 2014.
- Hybrid Simulation Network for Vehicular Ad Hoc Network (Vanet)
Abstract Views :212 |
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Authors
Affiliations
1 Simulation and Computing Laboratory, Universiti Malaysia Sabah, MY
1 Simulation and Computing Laboratory, Universiti Malaysia Sabah, MY
Source
ICTACT Journal on Communication Technology, Vol 9, No 1 (2018), Pagination: 1686-1695Abstract
Intelligent Transportation Systems (ITS) plays a vital role in providing different means of traffic management and enables users to be better informed of traffic condition, promoting safer, coordinated and efficient use of transport network. Vehicular Ad Hoc Network (VANET) shows promising reliability and validity in ITS. But, it poses challenges to researchers in designing protocol specifically for VANET as the deployment of VANET in real world will incur high cost. Therefore, simulation and non-physical testbed implementation have been widely adopted by the VANET research community in the development and assessment of the new or improved system and protocol of VANET. This paper presents a viable simulation platform for network development. Besides, a code cast or better known as network coding, a data packet transmission method has been developed and introduced into VANET protocol using the presented platform to assess and determine the potential of the introduced simulation platform.Keywords
Vehicular Ad hoc Network, Traffic Simulation, Network Simulation, Intelligent Transportation System, Network Coding.References
- M. Seredynski, G. Danoy, M. Tabatabaei, P. Bouvry and Y. Pigne, “Generation of Realistic Mobility for VANETs using Genetic Algorithms”, Proceedings of IEEE Congress on Evolutionary Computation, pp. 334-337, 2012.
- F. Cunha, L. Villas, A. Boukerche, G. Maia, A. Viana, R.A.F. Mini and A.A.F. Loureiro, “Data Communication in VANETs: Protocols, Applications and Challenges”, Ad Hoc Networks, Vol. 44, pp. 90-103, 2016.
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- S.E. Tan, H.T. Yew, M.S. Arifianto, I. Saad and K.T.K. Teo, “Queue Management for Network Coding in Ad Hoc Networks”, Proceedings of 3rd International Conference on Intelligent Systems Modelling and Simulation, pp. 657-662, 2012.
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- Y.S. Chia, Z.W. Siew, H.T. Yew, S.S. Yang and K.T.K. Teo, “An Evolutionary Algorithm for Channel Assignment Problem in Wireless Mobile Networks”, ICTACT Journal on Communication Technology, Vol. 3, No. 4, pp. 613-618, 2012.
- S.E. Tan, Z.W. Siew, Y.K. Chin, S.C.K. Lye and K.T.K. Teo, “Minimizing Network Coding Nodes in Multicast Tree Construction Via Genetic Algorithm”, Proceedings of 4th International Conference on Computational Intelligence, Communication Systems and Networks, pp. 399-404, 2012.
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- Differential Evolution Based Maximum Power Point Tracker for Photovoltaic Array Under Non-Uniform Illumination Condition
Abstract Views :192 |
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Authors
Nurul Izyan Kamaruddina
1,
Ahmad Razani Haron
1,
Bih Lii Chua
1,
Min Keng Tan
1,
Kit Guan Lim
1,
Kenneth Tze Kin Teo
1
Affiliations
1 Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, MY
1 Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, MY
Source
ICTACT Journal on Soft Computing, Vol 10, No 3 (2020), Pagination: 2076-2083Abstract
Photovoltaic system (PV) is an important technological asset for renewable energy production. It works by converting solar cell energy from the sun into electrical direct current. In reality, the photovoltaic module usually receives non-uniform solar irradiance at different light intensity due to non-atmospheric hindrance. Under such conditions, the PV system exhibits multiple peaks on the energy characteristic curve, generally known as the partial shading condition (PSC). Therefore, in order to maximize the energy harvested by the photovoltaic system (PV), maximum power point tracking (MPPT) method is suggested to extract all possible maxima that have been produced by the PV system under various circumstances through the non-uniform irradiance of the sunlight. Based on previous researches, it is found that conventional method such as perturb and observed (P&O) method failed to track the maximum power and was trapped at the local maximum power (LMPP). This paper focuses on exploring a metaheuristic method which is the differential evolution (DE) algorithm in optimizing the energy harvested by the PV system. The platform chosen for modelling in this paper is a 3 × 3 PV array. The PV array is tested with different conditions of partial shading where random irradiance values are set. Comparing the performance of PV between P&O and DE based MPPT controller, the DE based MPPT controller is inferred to have a higher success rate to escape from being trapped in LMPP and thus produce more total energy.Keywords
Photovoltaic system, Maximum Power Point Tracking, Partial Shading Condition, Perturb and Observed, Differential Evolution.References
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- Comparison Study of Deterministic and Metaheuristic Algorithms for Stochastic Traffic Flow Optimization Under Saturated Condition
Abstract Views :206 |
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Authors
Min Keng Tana
1,
Helen Sin Ee Chuo
1,
Kit Guan Lim
1,
Renee Ka Yin Chin
1,
Soo Siang Yang
1,
Kenneth Tze Kin Teo
1
Affiliations
1 Modelling Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, MY
1 Modelling Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, MY
Source
ICTACT Journal on Soft Computing, Vol 10, No 3 (2020), Pagination: 2117-2123Abstract
Traffic congestion is a perennial issue for most cities. Various artificial intelligence (AI) algorithms, which can categorize as deterministic and metaheuristic algorithms have been suggested to mitigate congestion. Although traffic flow is dynamic and stochastic in nature, most of the previous works evaluated the algorithms with a deterministic or non-stochastic traffic flow pattern. As such, the adaptiveness of those AI algorithms in dealing with stochastic traffic flow patterns is yet to be investigated. Therefore, this paper aims to explore the feasibility of both algorithm types in controlling stochastic traffic flow. In this work, a benchmarked traffic flow model is modified and developed as the simulation platform with the parameters extracted from the guidelines of Public Works Department Malaysia (JKR). Normal distribution function is embedded in the developed model to simulate non-uniform headway for inflow and outflow vehicles. Two commonly used algorithms, namely Fuzzy Logic and Genetic Algorithm are proposed as the adaptive controller to optimize the traffic signalization based on the instant stochastic traffic demand. The simulation results show the metaheuristic algorithm performs better than the deterministic algorithm. The mutation mechanism of the metaheuristic algorithm improves the exploration ability of the algorithm in seeking the optimum solution within the solution space without bounded by a set of fixed-computational rules.Keywords
Genetic Algorithm, Fuzzy Logic, Signal Optimization, Stochastic Flow, Saturated Condition.References
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Authors
Vincent Chung
1,
Hamzarul Alif Hamzah
1,
Norah Tuah
2,
Kit Guan Lim
1,
Min Keng Tan
1,
Kenneth Tze Kin Teo
1
Affiliations
1 Simulation and Computing Laboratory, Faculty of Engineering, Universiti Malaysia Sabah, MY
2 Faculty of Computer Science and Mathematics, Universiti Teknologi MARA, MY
1 Simulation and Computing Laboratory, Faculty of Engineering, Universiti Malaysia Sabah, MY
2 Faculty of Computer Science and Mathematics, Universiti Teknologi MARA, MY