Advances in Natural Computing: Methods and Applications

Dear Colleagues,

Natural computing, as a cutting-edge computational paradigm, has made significant progress in both theoretical research and practical applications in recent years. Drawing inspiration from natural phenomena, biological mechanisms, and physicochemical processes, it has developed nature-inspired computational models and meta-heuristic algorithms, providing innovative solutions for complex problems that are challenging for traditional computing methods. These approaches harness principles such as evolution, swarm intelligence, self-organization, and adaptability, enabling breakthroughs in optimization, learning, and system design. Research in natural computing not only advances computer science but also fosters interdisciplinary collaborations with fields like artificial intelligence, bioinformatics, physics, and quantum computing, demonstrating vast potential for cross-domain innovation. We welcome submissions on topics including but not limited to the following: Theoretical foundations of natural computing, including nature-inspired models and the development of novel meta-heuristic algorithms. Applications of natural computing in artificial intelligence (e.g., neural networks, reinforcement learning). Applications in bioinformatics (e.g., sequence analysis, protein structure prediction). Meta-heuristic algorithms for optimization problems in engineering, logistics, and industry. Synergies between natural computing and quantum computing. The modeling and analysis of complex systems using nature-inspired approaches. By integrating nature-inspired principles and meta-heuristic strategies, this Topic aims to highlight advancements that bridge computational theory with real-world challenges. We encourage contributions that explore both foundational innovations and practical implementations.

Prof. Dr. Gaige Wang
Prof. Xiao-Zhi Gao
Dr. Ying Tian
Topic Editors