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

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.

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 Sahin, 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

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.