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This book is a PhD dissertation which focuses on the study, design, development and application of centralised cooperative strategies for optimisation problems. These models consist of a set of parallel cooperating agents, where each agent carries out a search in a solution space sharing information with the rest of the agents. Many studies have shown that this cooperation leads to more efficient and effective strategies. The authors start describing the most known trajectory and population-based metaheuristics and introduce the cooperative strategies. The core of this book is devoted to the following topics: analysis of the composition and the cooperation scheme, hybridization with Reactive Search ideas, application of centralised cooperative strategies to Dynamic Optimisation Problems and resolution of sets of instances by a cooperative method. The book is addressed to students or researchers in the fields of Intelligent Systems, Soft Computing, Optimisation Techniques and, especially, Hybrid Metaheuristics.
| Autorius: | Antonio D. Masegosa, David A. Pelta, José L. Verdegay, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2011 |
| Knygos puslapių skaičius: | 220 |
| ISBN-10: | 3844324585 |
| ISBN-13: | 9783844324587 |
| Formatas: | 220 x 150 x 14 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Cooperative Methods in Optimisation: Analysis and Results“
