Handbook of Moth-Flame Optimization Algorithm: Variants, Hybrids, Improvements, and Applications

The Moth-Flame Optimization algorithm is a widely used meta-heuristic for solving optimization problems, but it faces challenges such as multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters. Handbook of Moth-Flame Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides an in-depth analysis of the algorithm and existing methods to cope with these challenges. It offers variants, hybrids, and applications in various areas, such as binary, multi-objective, noisy, dynamic, and combinatorial optimization.

Format: Hardback
Length: 332 pages
Publication date: 20 September 2022
Publisher: Taylor & Francis Ltd

Moth-Flame Optimization (MFO) is a cutting-edge meta-heuristic that has gained widespread application in both scientific and industrial fields. The process of solving optimization problems with MFO presents several formidable challenges, including the need to handle multiple objectives, constraints, binary decision variables, vast search spaces, dynamic objective functions, and noisy parameters. Handbook of Moth-Flame Optimization Algorithm: Variants, Hybrids, Improvements, and Applications delves into these challenges in depth, offering a comprehensive examination of the MFO algorithm and the existing methods in the literature.

Key Features:

Comprehensive Review of the Literature: This handbook provides a thorough review of the literature surrounding the MFO algorithm. It covers the theoretical foundations, mathematical models, and mechanisms underlying the algorithm, providing a solid foundation for further exploration.

In-Depth Analysis of Equations, Mathematical Models, and Mechanisms: The handbook delves into the intricacies of the MFO algorithm, offering detailed explanations of the equations, mathematical models, and mechanisms employed. This analysis helps researchers and practitioners understand the inner workings of the algorithm and how it operates.

Variants and Hybrids of the MFO Algorithm: The handbook presents various variants of the MFO algorithm designed to tackle different optimization problems. These variants include single-objective, multi-objective, noisy, dynamic, and combinatorial optimization problems. Each variant is discussed in detail, highlighting its strengths and weaknesses and proposing potential improvements.

Design, Development, and Testing of Hybrids: The handbook demonstrates how to design, develop, and test different hybrids of the MFO algorithm. Hybrids combine the strengths of multiple algorithms to enhance the performance of the MFO algorithm in solving complex optimization problems.

Applications of the MFO Algorithm: The handbook explores several application areas of the MFO algorithm, including optimization in engineering, finance, logistics, and healthcare. It showcases real-world examples and case studies where the MFO algorithm has been successfully applied to solve challenging optimization problems.

Who Should Read This Handbook?

Researchers in evolutionary computation and meta-heuristics: This handbook is a valuable resource for researchers interested in exploring the potential of the MFO algorithm and developing new methods and variants. It provides a comprehensive overview of the literature and offers insights into the latest research developments.

Those Interested in Applying MFO Algorithm and Swarm Intelligence Methods: This handbook is also suitable for practitioners who are interested in applying MFO algorithm and swarm intelligence methods to different application areas. It provides practical guidance on designing, developing, and testing hybrid algorithms and showcases real-world applications of the MFO algorithm.

In conclusion, Handbook of Moth-Flame Optimization Algorithm: Variants, Hybrids, Improvements, and Applications is a comprehensive and authoritative guide to the MFO algorithm and its applications. It offers valuable insights into the challenges faced in optimization problems and provides practical solutions and strategies for addressing them. Whether you are a researcher, practitioner, or simply interested in learning more about optimization algorithms, this handbook is a must-read.

Weight: 800g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032070919