Handbook of Swarm Intelligence [electronic resource] : Concepts, Principles and Applications / edited by Bijaya Ketan Panigrahi, Yuhui Shi, Meng-Hiot Lim.

By: Panigrahi, Bijaya Ketan [editor.]Contributor(s): Shi, Yuhui [editor.] | Lim, Meng-Hiot [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Adaptation, Learning, and Optimization: 8Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Description: XII, 544 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642173905Subject(s): Engineering | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
Part A: Particle Swarm Optimization -- Part B: Bee Colony Optimization -- Part C: Ant  Colony Optimization.-Part D: Other Swarm Techniques.
In: Springer eBooksSummary: 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, albeit 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 particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization algorithm (BFOA), honey bee social foraging algorithms, and harmony search (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.
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Part A: Particle Swarm Optimization -- Part B: Bee Colony Optimization -- Part C: Ant  Colony Optimization.-Part D: Other Swarm Techniques.

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, albeit 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 particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization algorithm (BFOA), honey bee social foraging algorithms, and harmony search (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.

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