Handbook of Moth-Flame Optimization Algorithm

Variants, Hybrids, Improvements, and Applications

Author: Seyedali Mirjalili Series: Advances in Metaheuristics

Handbook of Moth-Flame Optimization Algorithm

Paperback

Moth-Flame Optimization algorithm is an emerging meta-heuristic published in 2015. This book provides in-depth analysis of this algorithm and the existing methods to cope with challenges. It proposes improvements, variants, and hybrids of this algorithm. Applications are also covered to demonstrate the applicability of methods in this book.

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Summary

Description

Moth-Flame Optimization algorithm is an emerging meta-heuristic and has been widely used in both science and industry. Solving optimization problem using this algorithm requires addressing a number of challenges, including 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 this algorithm and the existing methods in the literature to cope with such challenges.

Key Features:

Reviews the literature of the Moth-Flame Optimization algorithm
Provides an in-depth analysis of equations, mathematical models, and mechanisms of the Moth-Flame Optimization algorithm
Proposes different variants of the Moth-Flame Optimization algorithm to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems
Demonstrates how to design, develop, and test different hybrids of Moth-Flame Optimization algorithm
Introduces several applications areas of the Moth-Flame Optimization algorithm

This handbook will interest researchers in evolutionary computation and meta-heuristics and those who are interested in applying Moth-Flame Optimization algorithm and swarm intelligence methods overall to different application areas.

About the Author

Seyedali Mirjalili is a Professor at Torrens University Center for Artificial Intelligence Research and Optimization and internationally recognized for his advances in nature-inspired Artificial Intelligence (AI) techniques. He is the author of more than 300 publications including five books, 250 journal articles, 20 conference papers, and 30 book chapters. With more than 50,000 citations and H-index of 75, he is one of the most influential AI researchers in the world. From Google Scholar metrics, he is globally the most cited researcher in Optimization using AI techniques, which is his main area of expertise. Since 2019, he has been in the list of 1% highly-cited researchers and named as one of the most influential researchers in the world by Web of Science. In 2021, The Australian newspaper named him as the top researcher in Australia in three fields of Artificial Intelligence, Evolutionary Computation, and Fuzzy Systems. He is a senior member of IEEE and is serving as an editor of leading AI journals including Neurocomputing, Applied Soft Computing, Advances in Engineering Software, Computers in Biology and Medicine, Healthcare Analytics, and Applied Intelligence.