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Handbook of Moth-Flame Optimization Algorithm
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Handbook of Moth-Flame Optimization Algorithm : Variants, Hybrids, Improvements, and Applications

Book Details

Format Hardback or Cased Book
ISBN-10 1032070919
ISBN-13 9781032070919
Publisher Taylor & Francis Ltd
Imprint CRC Press
Country of Manufacture GB
Country of Publication GB
Publication Date Sep 20th, 2022
Print length 332 Pages
Weight 453 grams
Ksh 18,900.00
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Moth-Flame Optimization algorithm is an emerging meta-heuristic published in 2015. This book provides in-depth analysis of this algorithm and the existing methods to cope with challenges. It proposes improvements, variants, and hybrids of this algorithm. Applications are also covered to demonstrate the applicability of methods in this book.

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.


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