Handbook of Moth-Flame Optimization Algorithm

<p>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.</p><p>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.</p><p>Key Features:</p><ul> <li>Reviews the literature of the Moth-Flame Optimization algorithm</li> <li>Provides an in-depth analysis of equations mathematical models and mechanisms of the Moth-Flame Optimization algorithm</li> <li>Proposes different variants of the Moth-Flame Optimization algorithm to solve binary multi-objective noisy dynamic and combinatorial optimization problems</li> <li>Demonstrates how to design develop and test different hybrids of Moth-Flame Optimization algorithm</li> <li>Introduces several applications areas of the Moth-Flame Optimization algorithm</li> </ul><p>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.</p>