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Applied multi-objective optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied multi-objective optimizationedited by Nilanjan Dey.
other author:	Dey, Nilanjan.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	xi, 170 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Metaheuristics.
Online resource:	https://doi.org/10.1007/978-981-97-0353-1
ISBN:	9789819703531$q(electronic bk.)

Applied multi-objective optimization
Applied multi-objective optimization[electronic resource] /edited by Nilanjan Dey. - Singapore :Springer Nature Singapore :2024. - xi, 170 p. :ill. (some col.), digital ;24 cm. - Springer tracts in nature-inspired computing,2524-5538. - Springer tracts in nature-inspired computing..

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.

ISBN: 9789819703531$q(electronic bk.)

Standard No.: 10.1007/978-981-97-0353-1doiSubjects--Topical Terms:

757062
Metaheuristics.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 005.13

Applied multi-objective optimization
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505 0 $a 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.
520 $a 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.
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650 2 4 $a Algorithms. $3 184661
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