Table of Contents

Section I Moth-Flame Optimization Algorithm for Different Optimization Problems

Chapter 1 ◾ Optimization and Meta-heuristics

Seyedali Mirjalili

Chapter 2 ◾ Moth-Flame Optimization Algorithm for Feature Selection: A Review and Future Trends

Qasem Al-Tashi, Seyedali Mirjalili, Jia Wu, Said Jadid Abdulkadir, Tareq M. Shami, Nima Khodadadi, and Alawi Alqushaibi

Chapter 3 ◾ An Efficient Binary Moth-Flame Optimization Algorithm with Cauchy Mutation for Solving the Graph Coloring Problem

Yass ine Meraihi, Asm a Benmess aoud Gabis, and Seyedali Mirjalili

Chapter 4 ◾ Evolving Deep Neural Network by Customized Moth-Flame Optimization Algorithm for Underwater Targets Recognition

Mohamm ad Khishe, Mokhtar Mohamm adi, Tarik A. Rashid, Hoger Mahmud, and Seyedali Mirjalili

Section II Variants of Moth-Flame Optimization Algorithm

Chapter 5 ◾ Multi-objective Moth-Flame Optimization Algorithm for Engineering Problems

Nima Khodadadi, Seyed Mohamm ad Mirjalili, and Seyedali Mirjalili

Chapter 6 ◾ Accelerating Optimization Using Vectorized Moth-Flame Optimizer (vMFO)

AmirPouya Hemm asian, Kazem Meidani, Seyedali Mirjalili, and Amir Barati Farimani

Chapter 7 ◾ A Modified Moth-Flame Optimization Algorithm for Image Segmentation

Sanjoy Chakraborty, Sukanta Nama, Apu Kumar Saha, and Seyedali Mirjalili

Chapter 8 ◾ Moth-Flame Optimization-Based Deep

Feature Selection for Cardiovascular Disease Detection Using ECG Signal

Arindam Majee, Shreya Bisw as, Somnath Chatterjee, Shibaprasad Sen, Seyedali Mirjalili, and Ram Sarkar

Section III Hybrids and Improvements of Moth-Flame Optimization Algorithm

Chapter 9 ◾ Hybrid Moth-Flame Optimization Algorithm with Slime Mold Algorithm for Global Optimization

Sukanta Nama, Sanjoy Chakraborty, Apu Kumar Saha, and Seyedali Mirjalili

Chapter 10 ◾ Hybrid Aquila Optimizer with Moth-Flame Optimization Algorithm for Global Optimization

Laith Abualigah, Seyedali Mirjalili, Mohamed Abd Elaziz, Heming Jia, Canan Batur Şahin, Ala’ Khalifeh, and Amir H. Gandomi

Chapter 11 ◾ Boosting Moth-Flame Optimization Algorithm by Arithmetic Optimization Algorithm for Data Clustering

Laith Abualigah, Seyedali Mirjalili, Mohamm ed Otair, Putra Sumari, Mohamed Abd Elaziz, Heming Jia, and Amir H. Gandomi

Section IV Applications of Moth-Flame Optimization Algorithm

Chapter 12 ◾ Moth-Flame Optimization Algorithm, Arithmetic Optimization Algorithm, Aquila Optimizer, Gray Wolf Optimizer, and Sine Cosine Algorithm: A Comparative Analysis Using Multilevel Thresholding Image Segmentation Problems

Laith Abualigah, Nada Khalil Al-Okbi, Seyedali Mirjalili, Mohamm ad Alshinwan, Husam Al Hamad, Ahmad M. Khasawneh, Waheeb Abu-Ulbeh, Mohamed Abd Elaziz, Heming Jia, and Amir H. Gandomi

Chapter 13 ◾ Optimal Design of Truss Structures with Continuous Variable Using Moth-Flame Optimization

Nima Khodadadi, Seyed Mohamm ad Mirjalili, and Seyedali Mirjalili

Chapter 14 ◾ Deep Feature Selection Using Moth-Flame Optimization for Facial Expression Recognition from Thermal Images

Ankan Bhattacharyya, Soumyajit Saha, Shibaprasad Sen, Seyedali Mirjalili, and Ram Sarkar

Chapter 15 ◾ Design Optimization of Photonic Crystal Filter Using Moth-Flame Optimization Algorithm

Seyed Mohamm ad Mirjalili, Somayeh Davar, Nima Khodadadi, and Seyedali Mirjalili

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.