Guest Editors
Dr. Namal Rathnayake
Email: namalhappy@gmail.com
Affiliation: Marine-Earth System Analytics Unit,Yokohama Institute for Earth Sciences (YES), Yokohama, 236-0001, Japan
Homepage:
Research Interests: prediction and forecasting, rainfall runoff, tropical cyclones, soft computing, data assimilation, optimization, robotics, artificial intelligence, machine learning, fuzzy logic
Dr. Yukinobu Hoshino
Email: hoshino.yukinobu@kochi-tech.ac.jp
Affiliation: School of Systems Engineering, Kochi University of Technology, Kochi, 782-8502, Japan
Homepage:
Research Interests: soft computing, system on a chip, intelligent system, fuzzy logic / fuzzy systems, fpga design, machine learning system, image processing system, embedded system, electronic circuit, robocup
Dr. Tuan Linh Dang
Email: linh.dangtuan@hust.edu.vn
Affiliation: School of Information and Communications Technology, Hanoi University of Science and Technology, Hanoi, Vietnam
Homepage:
Research Interests: object detection / object recognition, machine learning, computer vision, hardware software codesign, fpga, application of machine learning in network performance management
Summary
Optimization is central to computer science, driving tasks from model tuning to resource allocation in distributed systems. Traditional exact methods often falter with high-dimensional, multimodal, or dynamic search spaces. Nature-inspired metaheuristics, modeled on biological and physical phenomena, offer robust, flexible solutions. Particle Swarm Optimization (PSO) exemplifies simplicity and adaptability, complemented by Differential Evolution (DE), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and the Firefly Algorithm. Hybrid metaheuristics integrate diverse strategies to accelerate convergence, maintain diversity, and enhance resilience in complex environments.
Aim & Scope
This Special Issue solicits advances in nature-inspired optimization and practical deployments across computer science. We welcome algorithmic innovations, novel variants of PSO, DE, ACO, ABC, and Firefly Algorithms, as well as hybrid metaheuristics combining complementary mechanisms. Submissions may explore theoretical analyses, including convergence proofs, adaptive parameter control, stability modeling, and performance benchmarking on standard and custom testbeds. Contributions on scalable architectures, parallel, distributed, GPU-accelerated, cloud-native, and edge computing frameworks, are encouraged. Application-focused studies in smart grids, logistics, bioinformatics, wireless sensor networks, image and signal processing, and the Internet of Things are also invited.
Suggested Themes
· Algorithmic Innovations: PSO, DE, ACO, ABC, Firefly variants, and hybrid strategies
· Theoretical Foundations: Convergence analysis, adaptive parameter control, stability, and complexity studies
· Scalable Implementations: Parallel, distributed, GPU-accelerated, cloud-native, and edge computing architectures
· Real-World Applications: Smart grids, logistics, bioinformatics, wireless sensor networks, image and signal processing, IoT
· Interdisciplinary Integrations: Synergies with deep learning, quantum computing, and edge intelligence
By bridging theoretical insights with practical validations, this Special Issue aims to highlight the power of nature-inspired metaheuristics in addressing complex, dynamic problems. We invite original research articles, reviews, and case studies that demonstrate algorithmic efficacy, scalability, and real-world impact. Contributions integrating optimization methods with emerging paradigms such as deep learning, quantum computing, and edge intelligence are especially encouraged to foster innovative and adaptive solutions across computing disciplines.
Keywords
Particle Swarm Optimization, Nature-Inspired Algorithms, Metaheuristic Optimization, Hybrid Metaheuristics, Swarm Intelligence, Evolutionary Computation, Real-World Applications, Scalable Implementations
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