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Record Nr. |
UNINA9910845092203321 |
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Titolo |
Applied Multi-objective Optimization / / edited by Nilanjan Dey |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (179 pages) |
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Collana |
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Springer Tracts in Nature-Inspired Computing, , 2524-5538 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Mathematical optimization |
Algorithms |
Computational Intelligence |
Optimization |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. . |
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