ABSTRACT
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.
TABLE OF CONTENTS
part I|76 pages
Moth-Flame Optimization Algorithm for Different Optimization Problems
chapter Chapter 2|24 pages
Moth-Flame Optimization Algorithm for Feature Selection: A Review and Future Trends
part II|75 pages
Variants of Moth-Flame Optimization Algorithm
chapter Chapter 5|18 pages
Multi-objective Moth-Flame Optimization Algorithm for Engineering Problems
part III|85 pages
Hybrids and Improvements of Moth-Flame Optimization Algorithm
chapter Chapter 9|22 pages
Hybrid Moth-Flame Optimization Algorithm with Slime Mold Algorithm for Global Optimization
chapter Chapter 10|32 pages
Hybrid Aquila Optimizer with Moth-Flame Optimization Algorithm for Global Optimization
chapter Chapter 11|29 pages
Boosting Moth-Flame Optimization Algorithm by Arithmetic Optimization Algorithm for Data Clustering
part IV|84 pages
Applications of Moth-Flame Optimization Algorithm