Handbook of Research on Fireworks Algorithms and Swarm Intelligence
Ying Tan (Redaktør)
In recent years, swarm intelligence has become a popular computational approach among researchers working on optimization
problems throughout the globe. Several algorithms inside swarm intelligence have been implemented due to their application to real-world issues and other advantages. Les mer
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Vår pris:
4320,-
(Innbundet)
Fri frakt!
Leveringstid: Ikke i salg
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.
In recent years, swarm intelligence has become a popular computational approach among researchers working on optimization
problems throughout the globe. Several algorithms inside swarm intelligence have been implemented due to their application
to real-world issues and other advantages. A specific procedure, Fireworks Algorithm, is an emerging method that studies the
explosion process of fireworks within local areas. Applications of this developing program are undiscovered, and research
is necessary for scientists to fully understand the workings of this innovative system.
The Handbook of Research on Fireworks Algorithms and Swarm Intelligence is a pivotal reference source that provides vital research on theory analysis, improvements, and applications of fireworks algorithm. While highlighting topics such as convergence rate, parameter applications, and global optimization analysis, this publication explores up-to-date progress on the specific techniques of this algorithm. This book is ideally designed for researchers, data scientists, mathematicians, engineers, software developers, postgraduates, and academicians seeking coverage on this evolutionary computation method.
The Handbook of Research on Fireworks Algorithms and Swarm Intelligence is a pivotal reference source that provides vital research on theory analysis, improvements, and applications of fireworks algorithm. While highlighting topics such as convergence rate, parameter applications, and global optimization analysis, this publication explores up-to-date progress on the specific techniques of this algorithm. This book is ideally designed for researchers, data scientists, mathematicians, engineers, software developers, postgraduates, and academicians seeking coverage on this evolutionary computation method.