03649nam a22005415i 4500 978-981-97-0353-1 DE-He213 20251030111724.0 cr nn 008mamaa 240316s2024 si | s |||| 0|eng d 9789819703531 978-981-97-0353-1 10.1007/978-981-97-0353-1 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Applied Multi-objective Optimization [electronic resource] / edited by Nilanjan Dey. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XI, 170 p. 60 illus., 45 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Tracts in Nature-Inspired Computing, 2524-5538 Chapter 1 : An Introduction to Multi-objective Optimization using Meta-heuristic Algorithms: Techniques and Applications -- Chapter 2 : Counterfactual Explanations and Federated Learning for Multi-objective Optimization -- Chapter 3: Multi-objective Adaptive Guided Differential Evolution for Passively Controlled Structures Equipped with Tunned Mass Damper -- Chapter 4: Evolutionary Approaches for Multi-objective Optimization and Pareto-Optimal Solution Selection in Data Analytics -- Chapter 5 : Multi-objective Lichtenberg Algorithm for Optimum Design of Truss Structures -- Chapter 6 : Performance Analysis of Multi-objective Function-Based Fractional PID Controller for System Frequency Regulation -- Chapter 7: Multi-Modal Routing in Urban Transportation Networks using Multi-objective Quantum Particle Swarm -- Chapter 8 : Plant Leaf Disease Localization and Severity Measurement using Multi-objective Ant Colony Optimization -- Chapter 9 : Multi-objective Feature Selection: A Comprehensive Review -- Chapter 10 :Enhancing Feature Selection using Multi-objective Optimization Concept. The book explains basic ideas behind several kinds of applied multi-objective optimization and shows how it will be applied in practical contexts in the domain of healthcare, engineering design, and manufacturing. The book discusses how meta-heuristic algorithms are successful in resolving challenging, multi-objective optimization issues in various disciplines, including engineering, economics, medical and environmental management. The topic is useful for graduates, researchers and lecturers in optimization, engineering, management science and computer science. . Computational intelligence. 64052 Mathematical optimization. 65522 Algorithms. 58677 Computational Intelligence. 64052 Optimization. 65528 Algorithms. 58677 Dey, Nilanjan. editor. edt http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819703524 Printed edition: 9789819703548 Printed edition: 9789819703555 Springer Tracts in Nature-Inspired Computing, 2524-5538 71076 https://doi.org/10.1007/978-981-97-0353-1 ZDB-2-INR ZDB-2-SXIT 58372 58372