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Books > Science & Mathematics > Mathematics > Mathematical foundations
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Nature-Inspired Optimization Algorithms (Paperback)
Loot Price: R1,648
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Nature-Inspired Optimization Algorithms (Paperback)
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Nature-Inspired Optimization Algorithms, a comprehensive work on
the most popular optimization algorithms based on nature, starts
with an overview of optimization going from the classical to the
latest swarm intelligence algorithm. Nature has a rich abundance of
flora and fauna that inspired the development of optimization
techniques, providing us with simple solutions to complex problems
in an effective and adaptive manner. The study of the intelligent
survival strategies of animals, birds, and insects in a hostile and
ever-changing environment has led to the development of techniques
emulating their behavior. This book is a lucid description of
fifteen important existing optimization algorithms based on swarm
intelligence and superior in performance. It is a valuable resource
for engineers, researchers, faculty, and students who are devising
optimum solutions to any type of problem ranging from computer
science to economics and covering diverse areas that require
maximizing output and minimizing resources. This is the crux of all
optimization algorithms. Features: Detailed description of the
algorithms along with pseudocode and flowchart Easy translation to
program code that is also readily available in Mathworks website
for some of the algorithms Simple examples demonstrating the
optimization strategies are provided to enhance understanding
Standard applications and benchmark datasets for testing and
validating the algorithms are included This book is a reference for
undergraduate and post-graduate students. It will be useful to
faculty members teaching optimization. It is also a comprehensive
guide for researchers who are looking for optimizing resources in
attaining the best solution to a problem. The nature-inspired
optimization algorithms are unconventional, and this makes them
more efficient than their traditional counterparts.
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