Evolutionary Optimization Algorithms

This text is a comprehensive reference for evolutionary optimization techniques, covering single and multi-objective problems, with step-by-step solutions and software like MATLAB and Python. It is aimed at senior undergraduate and graduate students in electrical engineering, electronics engineering, mechanical engineering, and computer science and engineering.

Format: Hardback
Length: 261 pages
Publication date: 12 October 2021
Publisher: Taylor & Francis Ltd

This comprehensive reference text delves into the realm of evolutionary optimization techniques, offering a comprehensive exploration of methods for finding optimal solutions to both single and multi-objective problems. The text provides a detailed account of each evolutionary optimization algorithm, tracing its historical origins and presenting essential working equations. Additionally, it delves into various variants and hybrids of optimization techniques, offering a diverse perspective on the field.

The text takes a step-by-step approach, guiding readers through the solution of a problem using practical examples and software applications such as MATLAB and Python. It encompasses a wide range of important optimization algorithms, including single objective optimization, multi-objective optimization, heuristic optimization techniques, the shuffled frog leaping algorithm, the bacteria foraging algorithm, and the firefly algorithm.

Designed with senior undergraduate and graduate students in the fields of electrical engineering, electronics engineering, mechanical engineering, and computer science and engineering in mind, this text serves as a valuable resource. It offers a step-by-step solution for each evolutionary optimization algorithm, facilitating a deeper understanding of these techniques. Furthermore, the inclusion of flowcharts and graphics enhances the text's accessibility, making complex concepts more visually engaging.

The text delves into the historical backgrounds of each optimization technique, shedding light on the contributions of influential researchers and scholars. It presents working equations and mathematical formulations, providing a solid foundation for those seeking to apply these algorithms in practical scenarios. Additionally, the text discusses popular optimization techniques such as particle swarm optimization and genetic algorithm, highlighting their strengths and limitations.

By incorporating the latest software applications, such as Python and MATLAB, the text ensures that readers have access to the most up-to-date tools and resources for solving optimization problems. This practical approach empowers students to apply their knowledge and skills in real-world situations, contributing to their professional growth and development.

In conclusion, this comprehensive reference text serves as an invaluable resource for students and professionals seeking to delve into the world of evolutionary optimization techniques. It offers a thorough exploration of algorithms, variants, hybrids, and practical applications, empowering readers to find optimal solutions to complex problems and advance their understanding of the field.

Weight: 556g
Dimension: 159 x 242 x 23 (mm)
ISBN-13: 9780367750541