Overview
- Provides general strategies to improve the performance of existing metaheuristic optimization algorithms
- Is useful for researchers working in the area of optimization
- Aims post-graduate students in different fields of engineering
Part of the book series: Studies in Computational Intelligence (SCI, volume 1059)
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Table of contents (10 chapters)
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Front Matter
Keywords
About this book
The main purpose of the present book is to develop a general framework for population-based metaheuristics based on some basic concepts of set theory. The idea of the framework is to divide the population of individuals into subpopulations of identical sizes. Therefore, in each iteration of the search process, different subpopulations explore the search space independently but simultaneously. The framework aims to provide a suitable balance between exploration and exploitation during the search process. A few chapters containing algorithm-specific modifications of some state-of-the-art metaheuristics are also included to further enrich the book.
The present book is addressed to those scientists, engineers, and students who wish to explore the potentials of newly developed metaheuristics. The proposed metaheuristics are not only applicable to structural optimization problems but can also be used for other engineering optimization applications. The book is likely to be of interest to a wide range of engineers and students who deal with engineering optimization problems.
Authors and Affiliations
Bibliographic Information
Book Title: Advanced Metaheuristic Algorithms and Their Applications in Structural Optimization
Authors: Ali Kaveh, Kiarash Biabani Hamedani
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-031-13429-6
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-13428-9Published: 18 September 2022
Softcover ISBN: 978-3-031-13431-9Published: 19 September 2023
eBook ISBN: 978-3-031-13429-6Published: 17 September 2022
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: X, 362
Number of Illustrations: 1 b/w illustrations, 159 illustrations in colour
Topics: Computational Intelligence, Optimization