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Alfian Ma'arif
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Control Systems and Optimization Letters
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Control Systems and Optimization Letters is an open-access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of control and optimization, rapidly enabling a safe and sustainable interconnected human society. Control Systems and Optimization Letters accept scientifically sound and technically correct papers and provide valuable new knowledge to the mathematics and engineering communities. Theoretical work, experimental work, or case studies are all welcome. The journal also publishes survey papers. However, survey papers will be considered only with prior approval from the editor-in-chief and should provide additional insights into the topic surveyed rather than a mere compilation of known results. Topics on well-studied modern control and optimization methods, such as linear quadratic regulators, are within the scope of the journal. The Control Systems and Optimization Letters focus on control system development and solving problems using optimization algorithms to reach 17 Sustainable Development Goals (SDGs). The scope is linear control, nonlinear control, optimal control, adaptive control, robust control, geometry control, and intelligent control.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 11 Documents
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Search results for , issue "Vol 3, No 3 (2025)" : 11 Documents clear
Bio-Inspired Hybrid Control for Autonomous Vehicles: Improving Real-Time Navigation Through the Integration of ACO and PSO Uzzaman, Asif; Islam, Monirul; Ahmed, Shishir
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.204

Abstract

This research demonstrates nature-inspired control systems for the navigation of autonomous vehicles (AVs), utilizing algorithms derived from nature Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) to tackle challenges posed by dynamic environments. ACO is based on the pheromone trails of ants to facilitate adaptive route selection, PSO draws inspiration from bird flocking behavior for optimal pathfinding, and ABC imitates the division of labor seen in bee swarms for decentralized decision-making. A combined ACO-PSO model merges ACO's capability for local adaptability with PSO's ability for global convergence, allowing for real-time modifications to paths. Simulations conducted on the CARLA and SUMO platforms illustrate improvements in navigation stability and responsiveness, showcasing enhancements in trajectory smoothness by 15%, collision avoidance by 22%, and congestion reduction by 18% when faced with unexpected obstacles and variable traffic conditions. The findings support the notion that bio-inspired systems can serve as scalable and resilient alternatives to conventional algorithms, providing strong solutions for the emergence of next-generation AV technologies. This study connects biological concepts with artificial autonomy to develop intelligent transportation systems using hybrid algorithms and real-time adaptive learning. Biologically inspired models enhance decision-making in complex environments. However, limitations such as high computational complexity and challenges in scaling the system for real-world applications are acknowledged.
Pheasant Foraging Algorithm (PFA): A Bio-Inspired Approach for High-Dimensional Optimization Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.216

Abstract

This paper proposes an optimization algorithm based on pheasant foraging behavior, the Pheasant Foraging Algorithm (PFA). The algorithm simulates the collective cooperation and strategy selection of pheasant groups in the foraging process and is used to solve high-dimensional optimization problems. Based on the analysis of pheasant foraging patterns, an adaptive improvement strategy is proposed to improve local search efficiency while maintaining global search capabilities. Experimental results show that compared with classical optimization methods such as particle swarm optimization (PSO) and genetic algorithm (GA), the PFA algorithm has better performance on many standard optimization problems, stronger global search capabilities and more stable convergence performance. The core innovation of PFA lies in its adaptive improvement strategy, which dynamically adjusts search behavior based on environmental feedback to balance global exploration and local exploitation. Unlike PSO and GA, which often suffer from premature convergence or limited local refinement, PFA introduces role-based cooperation and adaptive flight mechanisms inspired by pheasant group foraging behavior. 
A Particle Swarm Optimization-Enhanced Support Vector Regression Model for Accurate Prediction of Concrete Compressive Strength Using Slump Test Data Al-Rammahi, Hussein; Asaad, Ameer Yalmaz
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.224

Abstract

This paper proposes a hybrid machine learning model combining Radial Basis Function (RBF) kernel-based Support Vector Regression (SVR) with Particle Swarm Optimization (PSO) to predict the compressive strength of concrete using slump test data. Conventional methods rely on labor- and resource-intensive destructive testing, posing challenges for large-scale projects. To address this, SVR models the nonlinear slump-strength relationship, while PSO (swarm size=50, 100 iterations) automates hyperparameter tuning. The SVR-PSO model is benchmarked against Decision Trees, Neural Networks, K-Nearest Neighbors (KNN), and Naïve Bayes, evaluated using R², MAE, MAPE, and RMSE. Results show SVR-PSO achieves  and the lowest error rates, reducing prediction costs by up to 40% compared to traditional testing. Limitations include the model’s validation on a specific concrete mix dataset; generalizability to broader formulations requires further study. For reproducibility, code and data will be made publicly available. This work demonstrates how PSO-optimized SVR enables faster, cost-effective strength estimation, supporting data-driven decisions in civil engineering.
Gaussian-Process-Augmented Particle Swarm Optimization (GP-PSO): Taxonomy, Survey, and Practitioner Guidance Perkasa, Sigit Dani; Jasmine, Senit Araminta; Fikri, Fachrizal
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.217

Abstract

High‐fidelity engineering simulations-Computational Fluid Dynamics, Finite‐Element Analysis and system‐dynamics models-impose prohibitive costs on optimization via traditional metaheuristics. Particle Swarm Optimization (PSO) and Gaussian Processes (GPs) have each shown promise, but canonical PSO suffers from premature convergence and excessive iterations when evaluations are noisy or expensive, and GP‐PSO integrations lack a unifying framework. In this review, we introduce a three‐axis taxonomy (1) surrogate‐integration strategy (e.g. fitness‐function replacement vs. augmentation), (2) acquisition (infill) function (e.g. Expected Improvement vs. Upper Confidence Bound), and (3) fidelity paradigm (e.g. single‐ vs. multi‐fidelity)-to classify and compare recent methods. We survey advances in deep‐kernel and neural‐augmented GPs, sparse‐GP approximations, adaptive retraining mechanisms, and hybrid/transfer‐learning extensions. Benchmark results on synthetic test suites and three real‐world applications (aerodynamic shape design, structural health monitoring, chemical‐process tuning) demonstrate 30–70 % fewer costly evaluations and 20–50 % faster convergence compared to PSO baselines, while maintaining or improving solution quality. From these studies, we distill practitioner guidelines for kernel and acquisition‐function selection, fidelity‐level choices, and reproducibility best practices-emphasizing shared code repositories and hyperparameter logs. Finally, we outline future directions in online surrogate updates, convergence theory under uncertainty, physics‐informed kernels, and standardized community benchmarks.
A Proposal for Designing a Novel Blockchain-Based Data Provisioning Mechanism for IoT Integration in Smart Applications Al-Rammahi, Hussein; Asaad, Ameer Yalmaz
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.222

Abstract

The Internet of Things (IoT) presents unique communication and security challenges due to the diverse nature and sheer volume of connected devices. Traditional communication solutions are often insufficient to meet these demands. This paper addresses the critical need for robust security in IoT by proposing a novel, blockchain-based mechanism for secure data provision in smart applications. While security is frequently overlooked in IoT in favor of application and hardware development, distributed ledger technology (blockchain) offers a promising solution. Beyond its role in cryptocurrencies, blockchain offers inherent capabilities for device identity, secure data transfer, and immutable data storage, all within a decentralized and transparent system that provides auditable cryptographic proofs. This paper aims to thoroughly analyze blockchain technology and evaluate prominent frameworks such as Ethereum and Bitcoin. We will examine the distinct features and target use cases of these frameworks to determine the most suitable blockchain architecture for the IoT ecosystem. This paper provides a high-level comparison of the evaluated architectures, alongside sample use cases and ongoing research, to aid developers and managers in selecting an appropriate framework based on their specific application requirements. The core contribution is the design and initial validation of our novel mechanism, which enhances data integrity and trust within IoT environments.
A Novel Incentive-Compatible Neural Network Optimization Model (ICNNOM) with Optimal Contract Structure Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.212

Abstract

In this paper, we propose a novel neural network optimization framework called the Incentive Compatible Neural Network Optimization Model (ICNNOM). This model combines the incentive compatibility idea in game theory with the optimal contract theory to simulate the "incentive and effort" mechanism between the internal layers of a deep neural network, aiming to improve the learning effect of the network. This paper uses two sets of codes with different architectures to conduct experiments on the CIFAR-10 and CIFAR-100 datasets and compares them with traditional neural network models. The results show that ICNNOM outperforms traditional models in multiple evaluation indicators such as accuracy, precision, recall, and F1 value, proving the effectiveness of introducing incentive mechanisms for model optimization. Incentive compatibility (IC) refers to designing mechanisms so that each participant's best interest aligns with truthful or cooperative behavior, while optimal contract theory studies designing agreements to maximize benefits under informational asymmetry. By integrating these concepts, ICNNOM explicitly coordinates the effort of each neural network layer to improve overall training consistency and efficiency.
Image Processing and Artificial Intelligence in Business Automation: A Review Laskar, Md Redwan; Chowdhury, Mizanul Hoque; Akter, Mahuma
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.221

Abstract

The integration of image processing and artificial intelligence (AI) is transforming business automation by enabling systems to interpret and act on visual data with human-like intelligence. This review explores the theoretical foundations and real-world applications of AI-driven image processing across industries such as manufacturing, healthcare, finance, logistics, and retail. Techniques like convolutional neural networks (CNNs), ResNet, YOLO, and Vision Transformers are used in tasks including defect detection, facial recognition, and document verification, yielding significant efficiency gains and cost reductions. Despite these benefits, challenges remain. These include a reliance on large annotated datasets, high computational demands (e.g., GPU costs), and limited model transparency. Ethical concerns such as bias in facial recognition and privacy issues in surveillance further complicate adoption. To address these, emerging solutions include the use of synthetic data (e.g., GANs), edge deployment for low-latency processing, and multimodal AI that combines image, text, and sensor inputs for deeper insights. Regulatory compliance with standards like GDPR and the EU AI Act is increasingly vital to ensure responsible use. This review presents a structured framework for integrating image processing with AI, outlining each stage from image acquisition to real-time decision-making and continuous learning. By highlighting current capabilities, limitations, and future trends, this paper encourages cross-industry collaboration and sustained RD investment to unlock the full potential of scalable, ethical, and intelligent automation in the age of Industry 4.0.
A Systematic Design of a Low-Cost Real-Time Vehicle Tracking System for Enhanced Security and Location Monitoring Marhoon, Hamzah M.; Sabah, Sabah Ali; Basil, Noorulden; Tarik, Benmessaoud Mohammed; Mohammed, Raghad Jassim; Fadhil Abbas, Riyam
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.219

Abstract

The rapid development of vehicle tracking technology has significantly enhanced the safety and security of vehicles worldwide. This paper presents the design and implementation of a real-time vehicle tracking system utilising Global Positioning System (GPS) and Global System for Mobile Communication (GSM) technologies, based on the Arduino Uno platform. The proposed system enables vehicle owners to continuously monitor their vehicle's location, receiving instant SMS notifications for unauthorised movement, speed violations, and historical location data. The system's core components include an Arduino Uno microcontroller, interfaced with a SIM900A GSM module and a NEO-6 M GPS module, enabling real-time tracking via SMS alerts and Google Maps integration. Key features include automatic alerts for unauthorised car startups, exceeding speed limits, live tracking requests, and location history retrieval. The system stores the last five GPS coordinates in EEPROM memory and offers a user-friendly interface for retrieving data via SMS. The integration of Google Maps enhances the tracking experience by providing a visual representation of the vehicle’s location. This solution offers a cost-effective and reliable means of vehicle monitoring, contributing to improved vehicle security and owner peace of mind.
Nelder-Mead Enhanced Gazelle Optimizer for Solving Complex Optimization Problems Yağız, Beytullah; Atar, Şeyma Nur; Eker, Erdal; Ekinci, Serdar; Izci, Davut
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.240

Abstract

This paper presents the improved gazelle optimization algorithm, which is a new approach in the field of metaheuristic optimization algorithms inspired by nature. By hybridizing the classical gazelle optimization algorithm with the Nelder-Mead simplex method, the improved gazelle optimization algorithm was developed. The proposed IGOA algorithm aims to combine GOA's global search capability with NM's local healing power to provide a more balanced and effective optimization of optimization problems. The performance of the algorithm was evaluated by 30 independent runs on the CEC2017 benchmark functions. The statistical results obtained from the analyses of the mean, standard deviation, best and worst values and Wilcoxon signed ranks test show that IGOA exhibits a superior or competitive performance compared to other current optimization algorithms. Furthermore, the boxplot and convergence curves revealed that IGOA exhibited stable convergence behavior and had a low tendency to get stuck at local optimums. Big-O analysis, on the other hand, confirmed that the algorithm can scale efficiently even in high-dimensional problems. The results prove that the IGOA algorithm is a highly competitive, effective and generalizable tool in solving complex optimization problems.
Advancing Microgrid Resilience Through Vehicle-to-Grid Integration: A Review of Current Trends and Future Directions Akash, Khairul Bashar; Ahmed, Shishir; Emon, Md Afrad Hasan; Shifat, Sk Md Raihan
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.220

Abstract

The integration of vehicle-to-grid (V2G) technology within microgrids is emerging as a transformative solution to enhance the resilience, reliability, and efficiency of modern energy systems. With electric vehicle (EV) adoption accelerating worldwide, V2G allows EVs to function not only as transportation assets but also as mobile, bidirectional energy storage resources capable of strengthening grid flexibility. By enabling EVs to discharge electricity back into the grid, V2G systems contribute to critical grid services such as peak shaving, load leveling, frequency regulation, and emergency backup during power disruptions. For microgrids, which are decentralized energy systems designed to operate either independently or in coordination with the main grid, the integration of V2G significantly improves stability, demand management, and the capacity to supply critical loads under adverse conditions. This dual functionality positions V2G as an important enabler of resilient, community-based energy networks. Nevertheless, widespread deployment faces challenges, including concerns over battery degradation, the absence of standardized interoperability, high infrastructure and implementation costs, and regulatory and market uncertainties. Furthermore, the full potential of V2G relies heavily on advancements in communication protocols, optimization-based energy management strategies, and intelligent control algorithms that can balance user preferences with system needs. This review examines the present state of V2G integration in microgrids, outlining its advantages, barriers, and future research directions, while emphasizing the importance of supportive regulations, large-scale pilot projects, and continued technological innovation in enabling the transition toward decentralized, decarbonized, and digitized energy systems

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