Glowworm Swarm Optimization

Theory, Algorithms, and Applications

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Programming
Cover of the book Glowworm Swarm Optimization by Krishnanand N. Kaipa, Debasish Ghose, Springer International Publishing
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Author: Krishnanand N. Kaipa, Debasish Ghose ISBN: 9783319515953
Publisher: Springer International Publishing Publication: January 10, 2017
Imprint: Springer Language: English
Author: Krishnanand N. Kaipa, Debasish Ghose
ISBN: 9783319515953
Publisher: Springer International Publishing
Publication: January 10, 2017
Imprint: Springer
Language: English

This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization.

 

The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intelligence and working on these topics.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization.

 

The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intelligence and working on these topics.

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