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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Nolia Harudin
Universiti Tenaga Nasional
Control & Systems Engineering Electrical & Electronics Engineering

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Taguchi's T-method with nearest integer-based binary bat algorithm for prediction Zulkifli Marlah Marlan; Khairur Rijal Jamaludin; Faizir Ramlie; Nolia Harudin
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3859

Abstract

Taguchi’s T-method is a new prediction technique under the Mahalanobis-Taguchi system to predict unknown output or future states based on available historical information. Conventionally, in optimizing the T-method prediction accuracy, Taguchi’s orthogonal array is utilized to determine a subset of significant features to be used in formulating the optimal prediction model. This, however, resulted in a sub-optimal prediction accuracy due to its fixed and limited feature combination offered for evaluation and lack of higher-order feature interaction. In this paper, a swarm-based binary bat optimization algorithm with a nearest integer discretization approach is integrated with the Taguchi’s T-method. A comparative study is conducted by comparing the performance of the proposed method against the conventional approach using mean absolute error as the performance measure on four benchmark case studies. The results from experimental studies show a significant improvement in the T-method prediction accuracy. A reduction in the total number of features results in a less complex model. Based on the general observation, the nearest integer-based binary bat algorithm successfully optimized the selection of significant features due to recursive and repetitive searchability, in addition to its adaptive element in response to the current best solution in guiding the search process towards optimality.
Enhanced Taguchi’s T-method using angle modulated Bat algorithm for prediction Zulkifli Marlah Marlan; Faizir Ramlie; Khairur Rijal Jamaludin; Nolia Harudin
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4350

Abstract

Analysis of multivariate historical information in predicting future state or unknown outcomes is the core function of Taguchi’s T-method. Introduced by Dr. Genichi Taguchi under Mahalanobis-Taguchi system, the T-method combines regression principle and robust quality engineering element in formulating a predictive model and employs taguchi’s orthogonal array design in optimizing the model through feature or variable selection process. There is a concern regarding the sub-optimality of the T-method prediction accuracy, particularly when the orthogonal array failed to offer a significant number of combinations in search for an optimal subset of features. This is due to the fixed and limited combination offered for evaluation as well as the lack of higher-order interaction of combination. In response to this issue, this paper proposed an angle modulated Bat algorithm to be integrated with the T-method in optimizing the prediction model. A comparison study was conducted using energy efficiency benchmark datasets with the mean absolute error metric used as the performance measure. The results show that the proposed method improved the prediction accuracy by 10.74%, from 6.05 to 5.4, by integrating only four features over the original eight in the prediction model.
Toward energy-efficient AGVs: A review of mechanical design contributions and optimization framework Amizi Noor; Khairur Rijal Jamaludin; Wan Zuki Azman Wan Muhamad; Faizir Ramlie; Nolia Harudin
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2070-2085

Abstract

Energy optimization in automated guided vehicles (AGVs) is critical for improving efficiency and sustainability in intralogistics systems, particularly in path planning and scheduling applications. Various optimization approaches have been proposed from operational, computational, and energy supply perspectives. Although energy supply technologies offer advantages in energy storage and recovery, their integration into AGV systems remains limited due to technological maturity and implementation challenges. Consequently, control-based approaches, including artificial intelligence, have become dominant optimization strategies. While these methods improve operational performance, they also increase computational energy demand, highlighting the need for alternative approaches to reduce baseline power consumption. This paper reviews AGV energy optimization studies while emphasizing the potential of mechanical design as an alternative optimization scope. The review reveals that mobility inefficiencies such as slip, skid, and instability are commonly mitigated through control strategies rather than resolved at their mechanical source. To address this gap, a Taguchi-based mechanical optimization framework is proposed for evaluating multiple mechanical factors and parameter levels. The framework aims to reduce baseline power demand and minimize reliance on computationally intensive control strategies, contributing toward more energy-efficient AGV systems.
Co-Authors Amizi Noor Faizir Ramlie Khairur Rijal Jamaludin Wan Zuki Azman Wan Muhamad Zulkifli Marlah Marlan