This book offers an extensive guide to understanding, implementing, and applying the Bees Algorithm, a powerful nature-inspired optimisation metaheuristic that mimics the foraging behaviour of honey bees. In today's highly interconnected world, systems have become more difficult to optimise. This book addresses the challenge of solving complex optimisation problems efficiently and effectively by drawing inspiration from the remarkable problem-solving abilities observed in nature. The Bees Algorithm provides an elegant, simple, robust, and adaptable approach to navigate the complexities of high-dimensional, multimodal, or time-varying problems that often stymie traditional optimisation methods. This book offers an in-depth exploration of the algorithm, providing a thorough understanding of its underlying principles and mechanisms. It establishes a mathematical framework for the algorithm, facilitating a clearer insight into its behaviour and performance. Through empirical studies and benchmarks, the book demonstrates the algorithm's effectiveness across a range of optimisation problems. Additionally, it showcases practical applications of the Bees Algorithm in diverse fields such as engineering design, robotics, and manufacturing. Finally, it discusses the latest developments and variants of the algorithm, highlighting its potential for future research and innovation. With its accessible style and step-by-step guidance, this book equips readersbe they researchers, practitioners, or students in computer science, engineering, or optimisationwith the knowledge and tools to leverage the principles of swarm intelligence and biomimicry to solve the real-world optimisation challenges of the new industrial age.

Provides a thorough understanding of the Bees Algorithm's principles and mechanisms Offers step-by-step instructions on implementing and applying the algorithm Demonstrates the algorithm's effectiveness through diverse case studies

Autorentext

Duc-Truong Pham is the Chance Professor of Engineering at the University of Birmingham. He was Professor of Computer-Controlled Manufacture and director of the Manufacturing Engineering Centre at Cardiff University. He has published over 600 papers and books on intelligent systems, advanced manufacturing, and remanufacturing and has graduated more than 100 Ph.D. students. His awards include five prizes from the Institution of Mechanical Engineers, a Lifetime Achievement Award from the World Automation Congress, and a Distinguished International Academic Contribution Award from the IEEE. He is a fellow of the Royal Academy of Engineering, Learned Society of Wales, SME, IET, and IMechE. He is the founding editor of the Springer Series in Advanced Manufacturing and editor-in-chief of Cogent Engineering and the International Journal on Interactive Design and Manufacturing. He obtained his Bachelor's, Ph.D., and D.Eng. degrees from the University of Canterbury (NZ). Marco Castellani, M.Sc., Ph.D., is an Associate Professor in the Department of Mechanical Engineering at the University of Birmingham. With over two decades of research experience, he has explored a diverse interdisciplinary landscape that integrates engineering, biology, and computer science. His work spans various application areas, including motor control, remote sensing, pattern recognition, optimisation, systems modelling, natural language processing, and ecological modelling. He has published nearly 80 research papers in scientific journals and presented at international conferences. Dr. Castellani has a strong interest in evolutionary and swarm intelligence, with a particular focus on the development and characterisation of the Bees Algorithm. A passionate communicator, he frequently delivers talks on the topic of nature-inspired intelligence. Luca Baronti, M.Sc., Ph.D., is a senior machine learning engineer at Kingfisher Plc, specialising in advanced AI solutions. Previously, he led technical development at Keyless Tech, where he pioneered deep learning applications for biometric systems and secured a patent for liveness anti-spoofing technology. Dr. Baronti has a strong background in both academic and industrial research and development. His career spans entrepreneurship—having founded an NLP-focused company—and significant contributions to semantic segmentation and optimisation techniques. An active member of the scientific community, Dr. Baronti has authored several peer-reviewed publications, contributes to open-source projects, and maintains expertise across multiple programming languages, cloud platforms, and machine learning frameworks. Beyond technology, he pursues drone photography and scuba diving in his free time.

Inhalt

Introduction.- The Bees algorithm.- Empirical study.- Analytical study.- Case study: Robot control optimisation.- Case study: PCB assembly optimisation.- Case study: Multi-solution problems.- Latest developments.- Appendices.