This book presents new and advanced results and developments related to the Bees Algorithm, along with its application to a wide range of engineering problems.
Modern complex processes and systems are difficult to optimise using conventional mathematical tools as they require models that often cannot be obtained with accuracy or certainty. Optimising such systems demands efficient, model-free optimisation tools.
The Bees Algorithm, a swarm-based technique inspired by the foraging behaviour of honeybees, is an ideal tool for tackling challenging optimisation problems. The algorithm is conceptually elegant and extremely easy to apply. All it needs to solve an optimisation problem is a means to evaluate the quality of potential solutions.
While the covered applications belong to diverse engineering fields, this book’s focus is on advanced manufacturing and industrial engineering. The book comprises two parts. The first part explores different enhancements made to the original Bees Algorithm to improve its performance.
The second part delves into the algorithm’s applications in design, manufacturing, production, ergonomics, logistics, transportation, and electrical and electronic engineering.
By showcasing the variety of optimisation tasks successfully handled using the Bees Algorithm, the book aims to inspire and motivate engineers and researchers worldwide to adopt the algorithm as a powerful and versatile tool for conquering complex engineering problems in the Industry 4.0 era and beyond.
Tabela de Conteúdo
Part 1: Bees Algorithm Development.- 1. Enhanced Bees Algorithm implementing early neighbourhood search with efficiency-based recruitment.- 2. Improving The Bees Algorithm Using Gradual Search Space Reduction.- 3. Local Optimal Issue in Bees Algorithm: Markov Chain Analysis and Integration with Dynamic Particle Swarm Optimisation Algorithm.- 4. Development of the Bees Algorithm Toolkit for Optimisation in Lab VIEW.- Part 2: Engineering Applications of the Bees Algorithm.– 5. Geometrical Optimisation of Smart Sandwich Plates Using The Bees Algorithm.- 6. Integrating the Bees Algorithm with WSAR for Search Direction Determination and Application to Constrained Design Optimisation Problems.- 7. Bees Algorithm-based optimisation of welding sequence to minimise distortion of thin-walled square Al-Mg-Si alloy tubes.- 8. Hybrid Genetic Bees Algorithm (GBA) for Continuous and Combinatorial Optimisation Problems.- 9. Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm.- 10. The Bees Algorithm for Robotics-enabled Collaborative Manufacturing.- 11. Bees Algorithm for Hyperparameter Search with Deep Learning to Estimate the Remaining Useful Life of Ball Bearings.- 12. Bees Local Phase Quantisation Feature Selection for RGB-D Facial Expression Recognition.- 13. Optimisation of Convolutional Neural Network Parameters using the Bees Algorithm.- 14. Ergonomic risk assessment combining the Bees Algorithm and simulation tools.- 15. A Knowledge Transfer-based Bees Algorithm for Expert Team Formation Problem in Internet Companies.- 16. Green Vehicle Routing Optimisation using the Bees Algorithm.- 17. Utilising the Bees Algorithm for UAV path planning – A simultaneous collision avoidance and shortest path approach.- 18. A Tabu-based Bees Algorithm for Unmanned Aerial Vehicles in Maritime Search and Rescue Path Planning.- 19. Pedestrian-Aware Cyber-Physical Optimisation of Hybrid Propulsion Systems using a Fuzzy Adaptive Cost Map and Bees Algorithm.- 20. Surrogate Model-Assisted Bees Algorithm for Global Optimisation of Microwave Filter.
Sobre o autor
Duc Truong Pham Ph.D. DEng holds the Chance Chair of Engineering at the University of Birmingham where he started his career as a lecturer in robotics and control engineering following undergraduate and postgraduate studies at the University of Canterbury in New Zealand. Before returning to Birmingham in 2011, he was Professor of Computer-Controlled Manufacture and Director of the Manufacturing Engineering Centre at Cardiff University. His research is in the areas of intelligent systems, robotics and autonomous systems, and advanced manufacturing technology. He has graduated more than 100 Ph.D. students and, together with them and other research collaborators, has published over 600 technical papers including the original article on the Bees Algorithm. He is a recipient of several awards, notably five best paper 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.
Natalia Hartono Ph.D. has been a lecturer in Indonesia since 2004 following undergraduate studies in Industrial Engineering at Maranatha Christian University and postgraduate studies in Industrial Engineering and Management at Bandung Institute of Technology, also in Indonesia. In 2019, she received a scholarship from the Indonesian Endowment Fund for Education for her Doctoral Studies at the University of Birmingham where she earned her doctorate in 2023. Her research interests include operations research, supply chain management, product planning and design, the Bees Algorithm, intelligent systems, remanufacturing, circular economy, sustainability modelling, and multi-criteria decision-making. She is part of the Bees Algorithm Research Group and Autonomous Remanufacturing Group at the University of Birmingham. Dr. Hartono is well-known as the co-chair of the International Workshop Series on the Bees Algorithm and Its Applications and the co-editor of the Springer book “Intelligent Production and Manufacturing Optimisation – The Bees Algorithm Approach.”
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