Editorial

摘要

Nature inspired optimisation algorithms (NIOA) are a set of biological tools and methodologies to address complex real-world problems to which traditional approaches may not be very effective. Major constituents of NIOA are neural networks, evolutionary algorithms, swarm intelligence algorithms, fuzzy systems, and hybrid intelligent systems. The applications of NIOA include bioinformatics and computational biology, brain-machine interface, digital eco-systems, healthcare and medical engineering, multi-media security and cyber security, robotics, design and manufacturing, energy and environment and many more. This special issue titled 'Nature inspired optimisation algorithms', of International Journal of Swarm Intelligence will provide a systematic overview of state-of-the-art research in the field of nature-inspired algorithms. This special issue aim to serve as a forum for facilitating and enhancing information sharing among researchers, with themes including the development of advanced nature-inspired algorithms and/or applying existing ones for solving problems in real world complex systems. It offers readers the articles on advances in the understanding and utilisation of systems those are based on the principles of nature inspired optimisation. Emphasis is given to such topics as evolutionary algorithms, swarm intelligence-based algorithms, hybridisation of nature-inspired optimisation algorithms, review and comparative studies of nature-inspired optimisation algorithms, new methodologies inspired from learningrnbehaviour of social insects/evolution processes; theory and practice of nature-inspired algorithms in different domains; and real-world problem solving using nature-inspired algorithms.