"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, noisy parameters, just to name a few. 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, meta-heuristics and to those who are interested in applying Moth-Flame Optimization algorithm and swarm intelligence methods overall to different application areas"--.