An introduction to metaheuristics for optimization
type: Item
instance of: scholarly article
title: An introduction to metaheuristics for optimization
zbMATH Open document ID: 1427.90001
DOI: 10.1007/978-3-319-93073-2
author: B. Chopard
author: Marco Tomassini
published in: Natural Computing Series
publication date: 2018-12-12T00:00:00Z
review text: The authors present a wide review of metaheuristics for optimization. The book covers most of the known efficient metaheuristic methods. It is organized in twelve chapters. The introductory Chapter 1 presents the basic information about problems, algorithms and complexity issues. Then, in Chapter 2, the authors discuss the concept of search spaces, including basic operations and search methods. Next, several metaheuristics are described: tabu search (Chapter 3), simulated annealing (Chapter 4), ant colony optimization (Chapter 5), particle swarm optimization (Chapter 6), fireflies and cuckoos (Chapter 7) and evolutionary methods (Chapters 8 and 9). At the end, the authors discuss some additional topics, not corresponding with any particular method (or rather corresponding with all of them). These are: phase transition in combinatorial problems (Chapter 10), empirical analysis of metaheuristics and their limitations (Chapter 11) as well as statistical analysis of the search space (Chapter 12). The book is self-contained. It is easy to understand, even for a reader who is not very familiar with mathematical tools (for that purpose the authors decided not to discuss more complex kinds of problems, like multiobjective programming problems). This allows to understand the main kinds of metaheuristics and major differences between them. For that reason, I would recommend this book for students in the area of operations research, but also for students and professionals from other fields (like natural sciences or social sciences) who would like not only to apply metaheuristics to solve the problems (which is rather easy nowadays because of a significant amount of specialized software available) but also to understand how they work.
reviewed by: Marcin Anholcer
Mathematics Subject Classification ID: 90-01
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 00A06
zbMATH DE Number: 6992877
zbMATH Keywords: metaheuristics
zbMATH Keywords: tabu search
zbMATH Keywords: simulated annealing
zbMATH Keywords: ant colony optimization
zbMATH Keywords: paticle swarm optimization
zbMATH Keywords: firefly algorithm
zbMATH Keywords: cuckoo search
zbMATH Keywords: evolutionary methods
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1007/978-3-319-93073-2
OpenAlex ID: W2899012101
description: scientific article; zbMATH DE number 6992877