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.