Springer Handbook of Nature-Inspired Optimization Algorithms - Volume I

The Handbook of Nature-Inspired Optimization Algorithms Volume I provides a comprehensive exploration of state-of-the-art methods for solving single-objective bound-constrained real-parameter optimisation problems. Published by Springer as part of the Systems, Decision and Control series (Volume 212), this authoritative reference synthesises current research and practical applications in nature-inspired computational techniques. The volume serves researchers, practitioners, and students in computer science, artificial intelligence, and machine learning who seek to understand and implement advanced optimisation strategies. Whether you are developing algorithms for engineering challenges, exploring biologically-motivated computation, or advancing your knowledge in AI and machine learning, this handbook delivers rigorous analysis, proven methodologies, and insights into how nature-inspired approaches can solve complex real-world problems efficiently.