ABSTRACT

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.

chapter 1|16 pages

Introduction

chapter 2|12 pages

Classical Optimization Methods

chapter 3|18 pages

Nature-Inspired Algorithms

chapter 4|14 pages

Genetic Algorithm

chapter 5|16 pages

Genetic Programming

chapter 6|12 pages

Particle Swarm Optimization

chapter 7|10 pages

Differential Evolution

chapter 8|16 pages

Ant Colony Optimization

chapter 9|16 pages

Bee Colony Optimization

chapter 10|12 pages

Fish School Search Algorithm

chapter 11|14 pages

Cuckoo Search Algorithm

chapter 12|10 pages

Firefly Algorithm

chapter 13|14 pages

Bat Algorithm

chapter 14|16 pages

Flower Pollination Algorithm

chapter 15|14 pages

Gray Wolf Optimization

chapter 16|8 pages

Elephant Herding Optimization

chapter 17|10 pages

Crow Search Algorithm

chapter 18|12 pages

Raven Roosting Optimization Algorithm

chapter 19|6 pages

Applications

chapter 20|6 pages

Conclusion