Differential Evolution: From Theory to Practice

This book summarizes the state-of-the-art research and development of differential evolution (DE) and its recent advances, including adaptive, self-adaptive, and hybrid techniques, for global optimization. It covers theoretical developments, performance comparisons, hybrid approaches, parallel and distributed DE, software implementations, and real-world applications. It is useful for researchers, practitioners, and students in optimization, heuristics, operations research, and natural computing.

Format: Paperback / softback
Length: 381 pages
Publication date: 27 January 2023
Publisher: Springer Verlag, Singapore

This comprehensive book delves into the cutting-edge research and advancements of differential evolution (DE), a powerful population-based meta-heuristic technique for global optimization. It encompasses a wide range of topics, including the development of adaptive, self-adaptive, and hybrid techniques, which have significantly enhanced the capabilities of DE in tackling complex problems.

Differential evolution is a versatile method that excels in handling non-differentiable, non-linear, and multi-modal objective functions, making it a valuable tool in various fields such as optimization, heuristics, operations research, and natural computing. The book showcases a collection of contributions that cover theoretical developments, performance comparisons, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications.

The theoretical developments in DE provide a solid foundation for understanding the underlying principles and mechanisms of the algorithm. The performance comparisons shed light on the effectiveness of DE in solving different optimization problems, highlighting its strengths and weaknesses. Hybrid DE approaches combine the advantages of other optimization techniques with DE, leading to improved solutions and better convergence rates.

Parallel and distributed DE techniques are crucial for solving large-scale optimization problems that require significant computational resources. The book discusses various strategies and algorithms for implementing parallel and distributed DE, enabling efficient optimization on high-performance computing platforms.

Software implementations of DE are essential for practical applications. The book provides detailed descriptions of various DE algorithms and their implementation in popular programming languages, making it easier for researchers and practitioners to utilize these techniques in their work.

Real-world applications demonstrate the practical significance of DE in various fields. The book showcases successful implementations of DE in optimization problems from different industries, such as engineering, finance, and healthcare.

This book is an invaluable resource for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research, and natural computing. It provides a comprehensive overview of the state-of-the-art research and development in DE, enabling readers to stay up-to-date with the latest advancements and apply these techniques to solve real-world problems effectively.

Weight: 605g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811680847
Edition number: 1st ed. 2022