Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications

ISBN-13: 9789813367722 / Angielski / Twarda / 2021 / 404 str.

Serdar Carbas; Abdurrahim Toktas; Deniz Ustun

Spis treści

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.

O autorze

Serdar Carbas received his B.Sc. in the Department of Civil Engineering from Ataturk University, Erzurum, Turkey, and his M.Sc. and Ph.D. in the Department of Engineering Sciences from Middle East Technical University (METU), Ankara, Turkey. He was  Visiting Scholar at the University of California, Los Angeles (UCLA), CA, USA (August 2011–September 2012). His current research fields cover the use of metaheuristic optimization techniques that are found quite effective in obtaining the solution of combinatorial optimization problems which are based on natural phenomena in the field of optimum design of structures. He has authored several book chapters and published more than 40 peer-reviewed research papers. He is Associate Professor at the Department of Civil Engineering in Karamanoglu Mehmetbey University, Karaman, Turkey. Also, he is Adjunct Associate Professor at the Civil Engineering Department of KTO Karatay University, Konya, Turkey.

Abdurrahim Toktas is Associate Professor at the Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey. He received B.Sc. degree in Electrical and Electronics Engineering at Gaziantep University, Gaziantep, Turkey, in July 2002. He worked as Telecom Expert from November 2003 to December 2009 for Turk Telecom Company which is the national PSTN and wideband Internet operator. He received M.Sc. and Ph.D. degrees in Electrical and Electronics Engineering at Mersin University, Mersin, Turkey, in January 2010 and July 2014, respectively. He worked as Network Expert in the Department of Information Technologies at Mersin University from December 2009 to January 2015. He is an editorial board member of the Journal of Recent Advances in Electrical & Electronic Engineering. He is the author of more than ninety research items involving articles, conference proceedings, and projects. His current research interests include electromagnetic modelling, computational electromagnetics, microstrip/printed antenna designing, radar absorber material modelling, design of MIMO antennas, design of UWB antennas, optimization algorithms, machine learnings, and surrogate model.

Deniz Ustun received his B.Sc. degree from the Department of Computer Science Engineering, Istanbul University, Istanbul, Turkey, in 2001. Besides, he received his M.Sc. and Ph.D. degrees from the Department of Electrical and Electronics Engineering, Mersin University, Mersin, Turkey, in 2009 and 2017, respectively. From 2003 to 2017, he was a senior lecturer in the Department of Software Engineering, Mersin University, Mersin, Turkey. Formerly, he was Assistant Professor in the Department of Computer Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey, between 2017 and 2020. He has been working for the Department of Computer Engineering, Tarsus University, Tarsus, Turkey, since March 2020, as Assistant Professor. His current research interests include heuristic and artificial intelligence-based optimization algorithms, surrogate models, machine learning, microstrip antennas, and so forth.

Blurb

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.