An introduction to metaheuristics for optimization
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.
scientific article; zbMATH DE number 6992877