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Nature-inspired metaheuristic algori...

Carbas, Serdar.

Nature-inspired metaheuristic algorithms for engineering optimization applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nature-inspired metaheuristic algorithms for engineering optimization applicationsedited by Serdar Carbas, Abdurrahim Toktas, Deniz Ustun.
other author:	Carbas, Serdar.
Published:	Singapore :Springer Singapore :2021.
Description:	xii, 404 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Nature-inspired algorithms.
Online resource:	https://doi.org/10.1007/978-981-33-6773-9
ISBN:	9789813367739$q(electronic bk.)

Nature-inspired metaheuristic algorithms for engineering optimization applications
Nature-inspired metaheuristic algorithms for engineering optimization applications[electronic resource] /edited by Serdar Carbas, Abdurrahim Toktas, Deniz Ustun. - Singapore :Springer Singapore :2021. - xii, 404 p. :ill., digital ;24 cm. - Springer tracts in nature-inspired computing,2524-552X. - Springer tracts in nature-inspired computing..

Introduction and Overview: Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications -- PART 1: Civil and Structural Engineering -- Harmony Search Algorithm for Structural Engineering Problems -- Teaching Learning Based Optimum Design of Transmission Tower Structures -- Modified Artificial Bee Colony Algorithm for Sizing Optimization of Truss Structures -- Electrostatic Discharge Algorithm for Optimum Design of Real Size Truss Structures -- Solving of Distinct Engineering Optimization Problems using Metaheuristic Algorithms -- The Design of Trapezoidal Corrugated Web Beams using Firefly Method -- Designing Fuzzy Controllers for Frame Structures Based on Ground Motion Prediction using Grasshopper Optimization Algorithm; A Case Study of Tabriz, Iran -- Optimization and Artificial Neural Network Models for Reinforced Concrete Members -- Statistical Investigation of the Robustness for the Optimization Algorithms -- Optimum Design of Beams with Varying Cross-Section by Using Application Interface -- Metaheuristic-based Structural Control Methods and Comparison of Applications -- Evolutionary Structural Optimization - A Trial Review -- An Extensive Review of Charged System Search Algorithm for Engineering Optimization Applications -- PART 2: Electrical and Electronics, Computer, and Communication Engineering -- Artificial Bee Colony Algorithm and Its Application to Content Filtering in Digital Communication -- Multi-objective Design of Multilayer Microwave Dielectric Filters using Artificial Bee Colony Algorithm -- Multi-objective Sparse Signal Reconstruction in Compressed Sensing -- Optimal Allocation of Flexible Alternative Current Transmission Systems: An Application of Particle Swarm Optimization.

This book engages in an ongoing topic, such as the implementation of nature-inspired metaheuristic algorithms, with a main concentration on optimization problems in different fields of engineering optimization applications. The chapters of the book provide concise overviews of various nature-inspired metaheuristic algorithms, defining their profits in obtaining the optimal solutions of tiresome engineering design problems that cannot be efficiently resolved via conventional mathematical-based techniques. Thus, the chapters report on advanced studies on the applications of not only the traditional, but also the contemporary certain nature-inspired metaheuristic algorithms to specific engineering optimization problems with single and multi-objectives. Harmony search, artificial bee colony, teaching learning-based optimization, electrostatic discharge, grasshopper, backtracking search, and interactive search are just some of the methods exhibited and consulted step by step in application contexts. The book is a perfect guide for graduate students, researchers, academicians, and professionals willing to use metaheuristic algorithms in engineering optimization applications.

ISBN: 9789813367739$q(electronic bk.)

Standard No.: 10.1007/978-981-33-6773-9doiSubjects--Topical Terms:

858794
Nature-inspired algorithms.

LC Class. No.: QA76.9.N37 / N38 2021

Dewey Class. No.: 005.13

Nature-inspired metaheuristic algorithms for engineering optimization applications
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505 0 $a Introduction and Overview: Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications -- PART 1: Civil and Structural Engineering -- Harmony Search Algorithm for Structural Engineering Problems -- Teaching Learning Based Optimum Design of Transmission Tower Structures -- Modified Artificial Bee Colony Algorithm for Sizing Optimization of Truss Structures -- Electrostatic Discharge Algorithm for Optimum Design of Real Size Truss Structures -- Solving of Distinct Engineering Optimization Problems using Metaheuristic Algorithms -- The Design of Trapezoidal Corrugated Web Beams using Firefly Method -- Designing Fuzzy Controllers for Frame Structures Based on Ground Motion Prediction using Grasshopper Optimization Algorithm; A Case Study of Tabriz, Iran -- Optimization and Artificial Neural Network Models for Reinforced Concrete Members -- Statistical Investigation of the Robustness for the Optimization Algorithms -- Optimum Design of Beams with Varying Cross-Section by Using Application Interface -- Metaheuristic-based Structural Control Methods and Comparison of Applications -- Evolutionary Structural Optimization - A Trial Review -- An Extensive Review of Charged System Search Algorithm for Engineering Optimization Applications -- PART 2: Electrical and Electronics, Computer, and Communication Engineering -- Artificial Bee Colony Algorithm and Its Application to Content Filtering in Digital Communication -- Multi-objective Design of Multilayer Microwave Dielectric Filters using Artificial Bee Colony Algorithm -- Multi-objective Sparse Signal Reconstruction in Compressed Sensing -- Optimal Allocation of Flexible Alternative Current Transmission Systems: An Application of Particle Swarm Optimization.
520 $a This book engages in an ongoing topic, such as the implementation of nature-inspired metaheuristic algorithms, with a main concentration on optimization problems in different fields of engineering optimization applications. The chapters of the book provide concise overviews of various nature-inspired metaheuristic algorithms, defining their profits in obtaining the optimal solutions of tiresome engineering design problems that cannot be efficiently resolved via conventional mathematical-based techniques. Thus, the chapters report on advanced studies on the applications of not only the traditional, but also the contemporary certain nature-inspired metaheuristic algorithms to specific engineering optimization problems with single and multi-objectives. Harmony search, artificial bee colony, teaching learning-based optimization, electrostatic discharge, grasshopper, backtracking search, and interactive search are just some of the methods exhibited and consulted step by step in application contexts. The book is a perfect guide for graduate students, researchers, academicians, and professionals willing to use metaheuristic algorithms in engineering optimization applications.
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