This book is specially focused on the latest developments and findings on hybrid algorithms and benchmarks in optimization and their applications in sciences, engineering, and industries. The book also provides some comprehensive reviews and surveys on implementations and coding aspects of benchmarks. The book is useful for Ph.D. students and researchers with a wide experience in the subject areas and also good reference for practitioners from academia and industrial applications.
This book is specially focused on the latest developments and findings on hybrid algorithms and benchmarks in optimization and their applications in sciences, engineering, and industries. The book also provides some comprehensive reviews and surveys on implementations and coding aspects of benchmarks. The book is useful for Ph.D. students and researchers with a wide experience in the subject areas and also good reference for practitioners from academia and industrial applications.
Xin-she Yang obtained his D.Phil. in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as Senior Research Scientist. Now he is Reader at Middlesex University London and Elected Fellow of Institute of Mathematics and its Applications (FIMA). He was IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management (2015-2020). He is also Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO). With more than 20 years of teaching and research experience, he has authored more than 10 books and edited more than 15 books. He has published more than 250 peer-reviewed research papers with nearly 75,000 citations. According to Clarivate Analytics/Web of Sciences, he has been on the prestigious list of highly cited researchers for seven consecutive years (2016-2022).
Inhaltsangabe
1. Nature-Inspired Algorithms: Overview and Open Problems.- 2. Hybrid algorithms: Components, Hybridization and Examples.- 3. Role of Benchmarks in Optimization.- 4. Travelling Salesman Problems: Symmetric and Asymmetric Cases.- 5. Scheduling Problems: Benchmarks and Implementation.- 6. Active Learning Solution for Semantic Labelling of Earth Observation Satellite Images.- 7. Development of an Ensemble Modelling Framework for Data Analytics in Supply Chain Management.- 8. An Application of Data Mining to Build the OD Matrix in Developing Countries: An Argentinean Case Study.- 9. Deep Learning-based Efficient Customer Segmentation for Online Retail Business.- 10. Application of a Routing Model with a Time Limit for the Collection of RSU in an Argentinian City.- 11. Network Weakness Detection: Case Studies.- 12. Unknown Target Searching by Swarm Robots: A Case Study.
1. Nature-Inspired Algorithms: Overview and Open Problems.- 2. Hybrid algorithms: Components, Hybridization and Examples.- 3. Role of Benchmarks in Optimization.- 4. Travelling Salesman Problems: Symmetric and Asymmetric Cases.- 5. Scheduling Problems: Benchmarks and Implementation.- 6. Active Learning Solution for Semantic Labelling of Earth Observation Satellite Images.- 7. Development of an Ensemble Modelling Framework for Data Analytics in Supply Chain Management.- 8. An Application of Data Mining to Build the OD Matrix in Developing Countries: An Argentinean Case Study.- 9. Deep Learning-based Efficient Customer Segmentation for Online Retail Business.- 10. Application of a Routing Model with a Time Limit for the Collection of RSU in an Argentinian City.- 11. Network Weakness Detection: Case Studies.- 12. Unknown Target Searching by Swarm Robots: A Case Study.
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