Nature-Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms
Nature-Inspired Optimization Algorithms by Vasuki A
English | 2020 | ISBN: 0367255985 | 272 Pages | PDF | 54 MB

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.

  • 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.