Guest Editors
Prof. Yeonwoo Lee
Email: ylee@mokpo.ac.kr
Affiliation: Department of Information Communication Engineering, Mokpo National University, Mokpo, South Korea
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Research Interests: applications of bio-inspired optimization algorithm, and artificial intelligence, smart-grid network, vehicular ad-hoc network, and cognitive radio systems.
Assoc. Prof. Gyanendra Prasad Joshi
Email: joshi@kangwon.ac.kr
Affiliation: Department of Electronic and AI System Engineering, Kangwon National University, Gangwon State, Samcheok-25913, South Korea
Homepage:
Research Interests: UAV localization, MAC and routing protocols for next-generation wireless networks, wireless sensor networks, cognitive radio networks, RFID systems, IoT, smart city, deep learning, and digital convergence
Prof. Seongsoo Cho
Email: css3617@gmail.com
Affiliation: Department of Software Engineering, Soongsil University, Seoul, 06978, Korea
Homepage:
Research Interests: computer networks, wireless sensor networks, digital contents, ubiquitous computing, mobile robotics, mobile control system, artificial intelligence, IoT device and system, ICT-covergence systems and, computing security & system
Summary
Bio-inspired optimization mimics evolution, swarming, and foraging to navigate vast, nonconvex search spaces efficiently. As cyber-physical systems and AI scale—and many tasks are NP-hard—exact or traditional methods become impractical, making these adaptive metaheuristics essential for robust, data-efficient decisions. Pairing bio-inspired techniques with quantum-enhanced approaches (e.g., QAOA hybrids) delivers scalable, high-quality solutions within acceptable time for modern systems.
This Special Issue addresses the challenges of selecting, designing, and applying effective bio-inspired optimization algorithms to diverse and complex problems. The choice of algorithm depends strongly on the characteristics of the problem, and quickly finding optimal or near-optimal solutions remains a key challenge in both theory and practice.
We invite original research and review articles on bio-inspired optimization, including evolutionary and swarm intelligence, brain- and plant-inspired models, and quantum-enhanced hybrids (e.g., QAOA-assisted, variational approaches). We particularly encourage cross-disciplinary integrations with machine learning and digital twin systems.
Topics include, but are not limited to:
· Novel bio-inspired algorithms and mechanisms (evolutionary, swarm, brain/plant-inspired)
· Applications of bio-inspired algorithms to VANET routing/spectrum management, smart grid & microgrid optimization (unit commitment, demand response), and healthcare/bioinformatics
· Theoretical analyses and comparative studies of bio-inspired and hybrid algorithms
· Hybrid quantum–bio-inspired frameworks (e.g., QAOA + GA/PSO/DE; quantum-assisted population initialization/selection)
Keywords
bio-inspired algorithms, artificial Intelligent, evolutionary algorithm, swarm, brain/plant-inspired algorithms, hybrid quantum–bio-inspired,
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