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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