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Particle swarm optimization. Translated from the French original. (English) Zbl 1130.90059
London: ISTE (ISBN 978-1-905209-04-0/hbk; 978-0-470-61216-3/ebook). 243 p. (2006).
Particle swarm optimization is yet another heuristic in the family of stochastic search methods formed by ant colony algorithms, evolutionary algorithms, genetic algorithms, simulated annealing, tabu search, just to mentioned a few of the best-known ones. In particle swarm optimization, a number of particles (forming a swarm) are moving in the search space, and the next move of a particle is a function of a) its current direction and velocity, b) the best position it has found so far, and c) the best position of its informants (a subset of the swarm). This is a basic formulation, and several variants occur. Particle swarm optimization seems more fitted for continuous optimization than discrete optimization.
Unfortunately, I cannot recommend this book. There are several reasons for this, and many of them are related to the publisher. It seems that the text has not been edited before publishing. Moreover, the English translation is unsatisfactory, even with some occasional words untranslated; the reader will soon learn what the French word “si” means. Finally, the mathematical typesetting is substandard, which in many places distracts reading.
The book is divided into two parts and has 18 chapters. Part I. Particle Swarm Optimization; Chapter 1. What is a Difficult Problem? Chapter 2. On a Table Corner; Chapter 3. First Formulations; Chapter 4. Benchmark Set; Chapter 5. Mistrusting Chance; Chapter 6. First Results; Chapter 7. Swarm: Memory and Graphs of Influence; Chapter 8. Distributions of Proximity; Chapter 9. Optimal Parameter Settings; Chapter 10. Adaptations; Chapter 11. TRIBES or Cooperation of Tribes; Chapter 12. On the Constraints; Chapter 13. Problems and Applications; Chapter 14. Conclusion. Part II. Outlines; Chapter 15. On Parallelism; Chapter 16. Combinatorial Problems; Chapter 17. Dynamics of a Swarm; Chapter 18. Techniques and Alternatives.
A reader with experience in metaheuristics and stochastic search will be able to filter out the main features of particle swarm optimization from the text. A less experienced reader, on the other hand, will possibly learn very little.

MSC:
90C59 Approximation methods and heuristics in mathematical programming
90-02 Research exposition (monographs, survey articles) pertaining to operations research and mathematical programming
65K05 Numerical mathematical programming methods
90C15 Stochastic programming
90C30 Nonlinear programming
90C11 Mixed integer programming
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