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Intelligent engineering optimisation with the bees algorithm

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Intelligent engineering optimisation with the bees algorithm/ edited by D. T. Pham, Natalia Hartono.
其他作者:	Pham, D. T.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xiii, 412 p. :ill. (some col.), digital ;24 cm.
內容註:	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 LabVIEW -- 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 OptimisationProblems -- 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.
Contained By:	Springer Nature eBook
標題:	Nature-inspired algorithms. -
電子資源:	https://doi.org/10.1007/978-3-031-64936-3
ISBN:	9783031649363

Intelligent engineering optimisation with the bees algorithm
Intelligent engineering optimisation with the bees algorithm[electronic resource] /edited by D. T. Pham, Natalia Hartono. - Cham :Springer Nature Switzerland :2025. - xiii, 412 p. :ill. (some col.), digital ;24 cm. - Springer series in advanced manufacturing,2196-1735. - Springer series in advanced manufacturing..

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 LabVIEW -- 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 OptimisationProblems -- 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.

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.

ISBN: 9783031649363

Standard No.: 10.1007/978-3-031-64936-3doiSubjects--Topical Terms:

3451303
Nature-inspired algorithms.

LC Class. No.: QA76.9.N37

Dewey Class. No.: 006.382

Intelligent engineering optimisation with the bees algorithm
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520 $a 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.
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