Nature-Inspired Algorithms: For Engineers and Scientists

by Krishn Kumar Mishra

Overview

This comprehensive reference text discusses nature inspired algorithms and their applications. It presents the methodology to write new algorithms with the help of MATLAB programs and instructions for better understanding of concepts. It covers well-known algorithms including evolutionary algorithms, genetic algorithm, particle Swarm optimization and differential evolution, and recent approached including gray wolf optimization. A separate chapter discusses test case generation using techniques such as particle swarm optimization, genetic algorithm, and differential evolution algorithm.

The book-

• Discusses in detail various nature inspired algorithms and their applications
• Provides MATLAB programs for the corresponding algorithm
• Presents methodology to write new algorithms
• Examines well-known algorithms like the genetic algorithm, particle swarm optimization and differential evolution, and recent approaches like gray wolf optimization.
• Provides conceptual linking of algorithms with theoretical concepts

The text will be useful for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering.

Discussing nature inspired algorithms and their applications in a single volume, this text will be useful as a reference text for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering. It discusses important algorithms including deterministic algorithms, randomized algorithms, evolutionary algorithms, particle swarm optimization, big bang big crunch (BB-BC) algorithm, genetic algorithm and grey wolf optimization algorithm. "

Product Details

ISBN-13:	9781032322643
Publisher:	CRC Press
Publication date:	10/08/2024
Pages:	326
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

K. K. Mishra is presently working as an assistant professor, department of computer science and engineering, Motilal Nehru National Institute of Technology Allahabad, India. His research areas include genetic algorithm, analysis of algorithm, automata theory, microprocessor and multi-objective optimization. He has taught courses including computer architecture, data structures, advanced computer architecture, programming in C++, microprocessor and automata theory at undergraduate and graduate level. He is a regular reviewer of the Journal of Supercomputing (Springer), Applied Intelligence, Applied Soft Computing, IEEE Transaction on Cybernetics, IEEE System Journal, Neural computing and application, and IETE journals.

Table of Contents

Preface. Acknowledgments. About the Author. Introduction. Binary Genetic Algorithms. Real-Parameter Genetic Algorithm. Differential Evolution. Particle Swarm Optimization. Grey Wolf Optimization. Environmental Adaptation Method. Other Important Optimization Algorithms. Application of Genetic Algorithms, Partial Swarm Optimization, and Differential Evolution in Software Testing. Application of Genetic Algorithms, Partial Swarm Optimization, and Differential Evolution in Regression Testing. Application of Genetic Algorithms and Partial Swarm Optimization in Cloud Computing. References and Further Reading. Index.