Mohd Herwan Sulaiman obtained his B. Eng. (Hons) in Electrical-Electronics, M. Eng (Electrical-Power) and PhD (Electrical Engineering) from Universiti Teknologi Malaysia (UTM) in 2002, 2007 and 2012 respectively. He is currently serves as an Associate Professor at Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang (UMP). His research interests are power system optimization and swarm intelligence applications to power system studies. He has authored and co-authored more than 100 technical papers in the international journals and conferences and also has been invited as a Journal reviewer for several international impact journals in the field of power system, soft computing application and many more. He is one of the inventors of the new nature inspired algorithm namely Barnacle Mating Optimizer. He is also a Senior Member of IEEE. His website can be accessed through .
RESEARCH INTERESTS
Power system optimization and swarm intelligence applications to power system studies
Machine learning for early detection of post-acute sequelae of COVID-19 (PASC): A comparative review of symptoms and risk factors Marzia Ahmed, Mohd Herwan Sulaiman, Md Shofiqul Islam, Shahrin Islam Franklin Open, 2026 SARS-CoV-2 is a multi-organ disease with a broad range of symptoms. Extensive research has been conducted to improve early detection, syndrome prediction, and diagnosis. However, the persistent condition experienced by recovered patients with COVID-19, known as post-acute sequelae of COVID-19 (PASC), remains underexplored. This review aims to analyze PASC symptoms, assess their risk intensity based on medical history, and highlight emerging variants. Unlike existing reviews, this article uniquely integrates machine learning techniques for personalized assessment of PASC risk and mapping of symptoms through an interactive platform. It introduces a conceptual framework that utilizes real-time patient data, enabling more accurate predictions and multidisciplinary treatment recommendations. The framework allows long-COVID patients to input symptoms via an app or website, which are then mapped against PASC datasets to assign risk levels (low, medium, or high). Machine learning models process these data for feature engineering and classification to predict the persistence of PASC. By leveraging machine learning for real-time risk stratification and treatment suggestions, this study advances post-COVID care beyond traditional symptom tracking. The proposed methodology is expected to outperform existing systems in predictive accuracy and patient-specific recommendations.
EMAPlus-optimized adaptive convergence prescribed performance control for high-precision steering of rack steering vehicles Addie Irawan, Norsharimie Mat Adam, Mohd Iskandar Putra Azahar, Mohd Zamri Ibrahim, Mohd Herwan Sulaiman Engineering Research Express, 2026 This paper presents an optimal Adaptive Convergence Prescribed Performance control cascaded with Anti-Windup PI (ACPPC-API) controller for steering-position control of rack steering vehicles (RSV) operating on cornering paths, optimized using the proposed Enhanced Evolutionary Mating Algorithm Lite (EMAPlus). The ACPPC framework regulates steering-error evolution through dynamically shaped convergence envelopes, while the Anti-Windup PI (AW-PI) inner loop stabilizes actuator behavior under saturation constraints. EMAPlus is employed to jointly tune the ACPPC and AW-PI parameters, enabling fast, stable, and computationally efficient optimization compared with the original Evolutionary Mating Algorithm (EMA), Ant Lion Optimizer (ALO), and Grasshopper Optimization Algorithm (GOA). Simulation results demonstrate that the EMAPlus-tuned ACPPC–API controller achieves the highest steering-tracking accuracy, reducing overshoot by up to 64%, suppressing residual ripple to within ±0.02 radians, and improving settling time by 20%–35% relative to the benchmark optimizers. These performance gains translate into superior vehicle-level responses, including 30%–60% lower curvature-tracking error, 45%–65% smaller sideslip deviation, and smoother lateral–yaw coordination during cornering maneuvers. Actuator-level and ride-quality indicators further reveal 35%–60% reductions in peak road-wheel rate and lateral jerk. Energy analysis confirms that more than 80% of the lateral–yaw kinetic energy is effectively directed into productive lateral-velocity motion with a shortened transient duration. The results establish the EMAPlus-optimized ACPPC–API controller as an efficient and robust steering solution for high-precision RSV cornering applications.
Tool wear classification in CNC machining via metaheuristic optimization of discrete neural network configurations Mohd Herwan Sulaiman, Zuriani Mustaffa, Mohd Razali Daud Engineering Research Express, 2025 Tool wear detection is essential for predictive maintenance in CNC machining systems, enabling early identification of worn tools to reduce defects, minimize unplanned downtime, and improve production efficiency. Traditional approaches, often relying on manual inspection or fixed thresholds, suffer from limited accuracy and adaptability. This study explores the use of metaheuristic optimized feedforward neural networks for automated tool wear classification using a publicly available CNC milling dataset. Three nature-inspired algorithms, namely Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), and Artificial Protozoa Optimization (APO), were employed to optimize discrete neural network parameters, including the number of hidden neurons (ranging from 5 to 100), hidden layer activation functions ( tansig , logsig , ReLU ), and output layer activation functions ( purelin , tansig , logsig ). Model performance was evaluated using accuracy, precision, recall, F 1 score, and AUC across five independent runs. The BMO-NN model achieved the highest average results, with an accuracy of 92.49 percent, precision of 91.86 percent, recall of 93.92 percent, and F 1 score of 92.88 percent. The best performing BMO-NN configuration used 100 hidden neurons with tansig activation functions in both layers. These findings highlight the potential of BMO based neural networks for robust and accurate tool condition monitoring in intelligent manufacturing.
Random forest based wind power prediction method for sustainable energy system Zuriani Mustaffa, Mohd Herwan Sulaiman Cleaner Energy Systems, 2025 Wind power generation prediction is critical for the effective integration of renewable energy into the power grid, supporting stability, reliability, and sustainability in electricity supply. However, the inherent variability and non-linear characteristics of wind patterns present substantial challenges to accurate prediction. This study tackles these challenges by utilizing the Random Forest (RF) algorithm, an ensemble learning approach renowned for its ability to capture complex, non-linear relationships in data. The RF model’s performance is compared with three commonly used prediction techniques: Neural Networks (NN), Extreme Gradient Boosting (XGBoost), and Linear Regression (LR). The models were evaluated using historical wind power data and key meteorological variables, with performance assessed through multiple metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Maximum Error (MAX), Standard Deviation (STD DEV), and R-squared (R²). The results indicate that the RF model achieved the best performance, with an RMSE of 55.11 and an R² of 0.9882, outperforming the NN, XGBoost, and LR models. Specifically, the NN model achieved an RMSE of 95.5 with an R² of 0.9651, XGBoost had an RMSE of 93.32 and an R² of 0.9666, and the LR model exhibited an RMSE of 144.45 with an R² of 0.9084. These findings demonstrate RF's superior predictive accuracy and robustness, making it a powerful tool for wind power forecasting, providing valuable insights for grid management and renewable energy planning.
Wind Turbine Lightning Protection Receptor with Sharp Needles Samer S. Wahdain, A. I. Mohamed, Mohd Herwan Sulaiman, AS Samsudin, Rahisham Abd Rahman 2024 IEEE International Conference on Advanced Power Engineering and Energy Empowering Advanced Power Engineering and Energy Apee 2024, 2024
Deep Learning-Based Technique for Sign Language Detection Zuriani Mustaffa, Nik Ahmad Farihin Mohd Zulkifli, Mohd Herwan Sulaiman, Ferda Ernawan, Yagoub Abbker Adam 2023 International Conference on Information Technology Research and Innovation Icitri 2023, 2023
Evolutionary mating algorithm Mohd Herwan Sulaiman, Zuriani Mustaffa, Mohd Mawardi Saari, Hamdan Daniyal, Seyedali Mirjalili Neural Computing and Applications, 2023
An application of hybrid swarm intelligence algorithms for dengue outbreak prediction Zuriani Mustaffa, Mohd Herwan Sulaiman, Mohamad Farhan Mohamad Mohsin, Yuhanis Yusof, Ferda Ernawan, Khairunnisa Amalina Mohd Rosli 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology Jeeit 2019 Proceedings, 2019
A Real-time Simulation Platform for Maximum Power Point Tracking Algorithm Study in Solar Photovoltaic System Applications of Modelling and Simulation, 2019
Barnacles Mating Optimizer Algorithm for Optimization Mohd Herwan Sulaiman, Zuriani Mustaffa, Mohd Mawardi Saari, Hamdan Daniyal, Ahmad Johari Mohamad, Mohd Rizal Othman, Mohd Rusllim Mohamed Lecture Notes in Electrical Engineering, 2019
Binary Search Algorithm-Based Maximum Power Point Tracking for Photovoltaic System under Partial Shaded Conditions 2018 20th European Conference on Power Electronics and Applications EPE 2018 Ecce Europe, 2018
Control on hydropower plant using fuzzy neural network based on right-angle triangle membership Journal of Advanced Research in Dynamical and Control Systems, 2018
Control of hydropower plant modeling using fuzzy neural network based on tuning membership function Journal of Advanced Research in Dynamical and Control Systems, 2018
Control on hydropower plant modeling using fuzzy neural network based on normalized firefly algorithm Journal of Advanced Research in Dynamical and Control Systems, 2018
Modeling of hydropower plant production using artificial neural network Journal of Advanced Research in Dynamical and Control Systems, 2018
Application of moth-flame optimizer and ant lion optimizer to solve optimal reactive power dispatch problems Journal of Telecommunication Electronic and Computer Engineering, 2018
Levenberg-Marquardt flood prediction for Sungai Isap residence Khoo Chun Keong, Mahfuzah Mustafa, Ahmad Johari Mohammad, Mohd Herwan Sulaiman, Nor Rul Hasma Abdullah, Rosdiyana Samad, Dwi Pebrianti Proceedings 2016 IEEE Conference on Systems Process and Control Icspc 2016, 2017
Artificial neural network flood prediction for sungai isap residence Khoo Chun Keong, Mahfuzah Mustafa, Ahmad Johari Mohammad, Mohd Herwan Sulaiman, Nor Rul Hasma Abdullah Proceedings 2016 IEEE International Conference on Automatic Control and Intelligent Systems I2cacis 2016, 2017
A review on photovoltaic array behavior, configuration strategies and models under mismatch conditions Arpn Journal of Engineering and Applied Sciences, 2016
Self balancing unicycle controlled by using arduino Arpn Journal of Engineering and Applied Sciences, 2016
Benchmark studies on Optimal Reactive Power Dispatch (ORPD) Based Multi-Objective Evolutionary Programming (MOEP) using Mutation Based on Adaptive Mutation Operator (AMO) and Polynomial Mutation Operator (PMO) Journal of Electrical Systems, 2016
Comparative study of p&o and modified incremental conductance algorithm in solar maximum power point tracking Iet Conference Publications, 2016
Load Frequency Control for hydropower plants using PID controller Journal of Telecommunication Electronic and Computer Engineering, 2016
Design and development of vanadium redox flow battery (V-RFB) cell stack Iet Conference Publications, 2016
Multi-Objective Evolutionary Programming (MOEP) using mutation based on Adaptive Mutation Operator (AMO) applied for optimal reactive power dispatch Arpn Journal of Engineering and Applied Sciences, 2016
Training LSSVM with GWO for price forecasting Zuriani Mustaffa, Mohd Herwan Sulaiman, Mohamad Nizam Mohmad Kahar 2015 4th International Conference on Informatics Electronics and Vision Iciev 2015, 2015
Price predictive analysis mechanism utilizing grey wolf optimizer-Least Squares Support Vector Machines Arpn Journal of Engineering and Applied Sciences, 2015
Inverse definite overcurrent relay discrimination algorithm and its application in industrial power systems Arpn Journal of Engineering and Applied Sciences, 2015
Performance analysis of active power filter for harmonic compensation using PI-PSO Arpn Journal of Engineering and Applied Sciences, 2015
Ant lion optimizer for optimal reactive power dispatch solution Journal of Electrical Systems, 2015
Grey wolf optimizer for solving economic dispatch problem with valve-loading effects Arpn Journal of Engineering and Applied Sciences, 2015
Solving Optimal Reactive Power planning problem utilizing nature inspired computing techniques Arpn Journal of Engineering and Applied Sciences, 2015
Computational intelligence technique for static var compensator (SVC) installation considering multicontingencies (N-m) Arpn Journal of Engineering and Applied Sciences, 2015
Effect of population size for DG installation using EMEFA S. R. A. Rahim, I. Musirin, M. M. Othman, M. H. Hussain, M. H. Sulaiman, A. Azmi Proceedings of the 2013 IEEE 7th International Power Engineering and Optimization Conference Peoco 2013, 2013
An application of differential search algorithm in solving non-convex economic dispatch problems with valve-point effects International Review on Modelling and Simulations, 2013
Assessing the performance of DG in distribution network S. R. A. Rahim, I. Musirin, M. H. Sulaiman, M. H. Hussain, A. Azmi 2012 IEEE International Power Engineering and Optimization Conference Peoco 2012 Conference Proceedings, 2012
Optimal embedded generation allocation in distribution system employing real coded genetic algorithm method World Academy of Science Engineering and Technology, 2010
Real and reactive power flow allocation in deregulated power system utilizing genetic-support vector machine technique International Review of Electrical Engineering, 2010
