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All Journal International Journal of Electrical and Computer Engineering Journal of Electronics, Electromedical Engineering, and Medical Informatics
Arshad, Mohd Rizal
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Impact of initialization of a modified particle swarm optimization on cooperative source searching Ab. Majid, Mad Helmi; Arshad, Mohd Rizal; Yahya, Mohd Faid; Ibrahim, Abu Bakar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp218-229

Abstract

Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization.
Comparative Evaluation of LSTM and Metaheuristic-Optimized Neural Networks for ECG Prediction under Limited Data Conditions Prenata, Giovanni Dimas; Ridho’i, Ahmad; Arshad, Mohd Rizal
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1524

Abstract

This study presents a comparative evaluation of Deep Feedforward Neural Network (DFFNN) models optimized using single-stage metaheuristic approaches, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), as well as a multi-stage hybrid optimization strategy (GA+GWO) for ECG-based emotion classification. The experimental dataset consists of ECG recordings collected from three elderly participants using a Sparkfun AD8232 sensor under controlled emotional stimuli, representing a limited-subject and small-data scenario. Feature extraction is conducted using Heart Rate Variability (HRV) parameters derived from both time domain (Mean RR, SDNN, RMSSD, Mean HR, and STD HR) and frequency domain (LF, HF, and LF/HF ratio). Experimental results from six repeated trials demonstrate that the multi-stage DFFNN+GA+GWO model achieves the best optimization performance, yielding the lowest Mean Squared Error (MSE) of 0.01599 and a consistent training accuracy of up to 85.71%. Compared with single-stage optimization methods, the hybrid approach exhibits improved convergence behavior and reduced performance variance, indicating enhanced optimization stability. However, test accuracy remains relatively limited (33.33%–50.00%), reflecting constrained generalization capability due to the small dataset and the absence of subject-wise or external validation. Further statistical analysis using confidence intervals and nonparametric testing confirms that the observed performance improvements are primarily associated with optimization stability rather than statistically significant gains in predictive generalization. Therefore, this study emphasizes the role of metaheuristic optimization in stabilizing neural network training under limited data conditions. The findings should be interpreted as a pilot feasibility study, and future work is required to validate the proposed approach using larger, more diverse datasets and more rigorous validation strategies.
Co-Authors Ab. Majid, Mad Helmi Abu Bakar Ibrahim Prenata, Giovanni Dimas Ridho’i, Ahmad Yahya, Mohd Faid