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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.

Table of Contents

1.Introduction 2. Classical Optimization Methods. 3. Nature inspired Algorithms. 4. Genetic Algorithm. 5. Genetic Programming. 6. Particle Swarm Optimization. 7. Differential Evolution 8. Ant Colony Optimization. 9. Bee Colony Optimization. 10. Fish School Search Optimization. 11. Cuckoo Search Algorithm 12. Firefly Algorithm. 13. Bat Algorithm. 14. Flower Pollination Algorithm. 15. Grey Wolf Optimization 16. Elephant Herding Algorithm. 17. Crow Search Algorithm. 18. Raven Roosting Optimization Algorithm 19. Applications 20. Conclusion.