Details
Handbook of Swarm Intelligence
Concepts, Principles and ApplicationsAdaptation, Learning, and Optimization, Band 8
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CHF 236.00 |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 04.02.2011 |
| ISBN/EAN: | 9783642173905 |
| Sprache: | englisch |
| Anzahl Seiten: | 544 |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<p>From nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more. It is only in recent years that researchers have taken notice of such natural swarming systems as culmination of some form of innate collective intelligence, <i>albeit</i> swarm intelligence (SI) - a metaphor that inspires a myriad of computational problem-solving techniques. In computational intelligence, swarm-like algorithms have been successfully applied to solve many real-world problems in engineering and sciences. This handbook volume serves as a useful foundational as well as consolidatory state-of-art collection of articles in the field from various researchers around the globe. It has a rich collection of contributions pertaining to the theoretical and empirical study of single and multi-objective variants of swarm intelligence based algorithms like <i>particle swarm optimization</i> (PSO), <i>ant colony optimization</i> (ACO), <i>bacterial foraging optimization algorithm</i> (BFOA), <i>honey bee social foraging algorithms</i>, and <i>harmony search</i> (HS). With chapters describing various applications of SI techniques in real-world engineering problems, this handbook can be a valuable resource for researchers and practitioners, giving an in-depth flavor of what SI is capable of achieving.</p>
<p>Part A: Particle Swarm Optimization.- Part B: Bee Colony Optimization.- Part C: Ant Colony Optimization.-Part D: Other Swarm Techniques.</p>
<p>From nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more. It is only in recent years that researchers have taken notice of such natural swarming systems as culmination of some form of innate collective intelligence, <i>albeit</i> swarm intelligence (SI) - a metaphor that inspires a myriad of computational problem-solving techniques. In computational intelligence, swarm-like algorithms have been successfully applied to solve many real-world problems in engineering and sciences. This handbook volume serves as a useful foundational as well as consolidatory state-of-art collection of articles in the field from various researchers around the globe. It has a rich collection of contributions pertaining to the theoretical and empirical study of single and multi-objective variants of swarm intelligence based algorithms like <i>particle swarm optimization</i> (PSO), <i>ant colony optimization</i> (ACO), <i>bacterial foraging optimization algorithm</i> (BFOA), <i>honey bee social foraging algorithms</i>, and <i>harmony search</i> (HS). With chapters describing various applications of SI techniques in real-world engineering problems, this handbook can be a valuable resource for researchers and practitioners, giving an in-depth flavor of what SI is capable of achieving.</p>
Comprehensive study of both theoretical and algorithmic analysis of swarm intelligence techniques. Provides real-world applications of SI techniques Written by leading experts in this field
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