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Engineering Applications of Modern Metaheuristics

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Cover of 'Engineering Applications of Modern Metaheuristics'

Table of Contents

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    Book Overview
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    Chapter 1 Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup
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    Chapter 2 Metaheuristic Algorithms in IoT: Optimized Edge Node Localization
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    Chapter 3 JAYA Algorithm Versus Differential Evolution: A Comparative Case Study on Optic Disc Localization in Eye Fundus Images
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    Chapter 4 Minimum Transmission Power Control for the Internet of Things with Swarm Intelligence Algorithms
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    Chapter 5 An Enhanced Gradient Based Optimized Controller for Load Frequency Control of a Two Area Automatic Generation Control System
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    Chapter 6 A Meta-Heuristic Algorithm Based on the Happiness Model
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    Chapter 7 Application of Metaheuristic Techniques for Enhancing the Financial Profitability of Wind Power Generation Systems
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    Chapter 8 Optimization of Demand Response
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    Chapter 9 Fitting Curves of Ruminal Degradation Using a Metaheuristic Approach
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    Chapter 10 Optimizing a Real Case Assembly Line Balancing Problem Using Various Techniques
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    Chapter 11 Multi-circle Detection Using Multimodal Optimization
Attention for Chapter 10: Optimizing a Real Case Assembly Line Balancing Problem Using Various Techniques
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Chapter title
Optimizing a Real Case Assembly Line Balancing Problem Using Various Techniques
Chapter number 10
Book title
Engineering Applications of Modern Metaheuristics
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-16832-1_10
Book ISBNs
978-3-03-116831-4, 978-3-03-116832-1
Authors

Mirzaei, Nima, Nejad, Mazyar Ghadiri

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 33%
Unknown 2 67%
Readers by discipline Count As %
Unspecified 1 33%
Unknown 2 67%

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