Multi-Objective Optimization : Evolutionary to Hybrid Framework / / edited by Jyotsna K. Mandal, Somnath Mukhopadhyay, Paramartha Dutta

• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2018
• Edizione: 1st ed. 2018.
• Descrizione fisica: 1 online resource (XVI, 318 p. 90 illus., 51 illus. in color.)
• Disciplina: 004.0151
• Soggetto topico: Computer science - Mathematics; Mathematical optimization; Computational intelligence; Mathematics of Computing; Optimization; Computational Intelligence
• Persona (resp. second.): MandalJyotsna K; MukhopadhyaySomnath; DuttaParamartha
• Nota di contenuto: Chapter 1. An Advance Overview of Single and Multi-Objective Optimization -- Chapter 2. Non-dominated Sorting Based Multi/Many Objective Optimization: Two Decades of Research and Application -- Chapter 3. Uncertain Multi-objective Portfolio Selection Model based on Genetic Algorithm -- Chapter 4. A Multiobjective Genetic Algorithm-based Approach for Identifying Relevant and Non-redundant Cancer-MicroRNA Markers -- Chapter 5. Application of Multi-objective Optimizations in Protein Structure Prediction -- Chapter 6. Multi-target Multiobjective Programming and Patrol Manpower Planning for Traffic Management via Genetic Algorithm -- Chapter 7. Multi-objective Optimization for Key Player Identification in Networks -- Chapter 8. Joint Maximization in Energy and Spectral Efficiency in Cooperative Cognitive Radio Networks -- Chapter 9. A Neoteric Multi-Objective Framework for Engineering Process Optimization: Metaheuristics and Experimental Designs based Approach -- Chapter 10. Multi/Many Objective Optimization – Hybrid Intelligent Framework -- Chapter 11. Efficiency Maximization of Multimedia Data Mining using Multiobjective Neuro-ACO Approach -- Chapter 12. Optimized Determination of Separating Hyper-Plane of an SVM – Hybrid Multiobjective Model -- Chapter 13. Efficient Cluster Head Selection in Wireless Sensor Network using Multiobjective Model -- Chapter 14. Achieving Optimized Bio-Metric Security in E-Governance by Multiobjective Neuro Approach -- Chapter 15. Advantage of Quantum Inspired Multiobjective Genetic Algorithm over Classical Multiobjective Genetic Algorithm -- Chapter 16. Optimizing Performance Parameter of Image Segmentation using Hybrid Multiobjective Framework.
• Sommario/riassunto: This book brings together the latest findings on efficient solutions of multi/many-objective optimization problems from the leading researchers in the field. The focus is on solving real-world optimization problems using strategies ranging from evolutionary to hybrid frameworks, and involving various computation platforms. The topics covered include solution frameworks using evolutionary to hybrid models in application areas like Analytics, Cancer Research, Traffic Management, Networks and Communications, E-Governance, Quantum Technology, Image Processing, etc. As such, the book offers a valuable resource for all postgraduate students and researchers interested in exploring solution frameworks for multi/many-objective optimization problems.
• Titolo autorizzato: Multi-Objective Optimization
• ISBN: 981-13-1471-3; 9789811314711
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910349472603321
• Lo trovi qui: Univ. Federico II