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Handbook of Moth-Flame Optimization Algorithm

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Benchmark Functions
cardiovascular disease detection
Category=PBT
Category=PBU
Category=UB
Category=UMB
Category=UYQ
Cauchy Distribution Function
Cauchy Mutation
Cuckoo Search
deep feature selection for thermal images
DNN Model
ECG Beat
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
evolutionary computation
Feature Selection
Friedman Ranking Test
Fs Method
Fs Technique
global optimisation techniques
Gray Wolf Optimizer
High Dimensional Search Spaces
IGD
image segmentation algorithms
Imperialism Competitive Algorithm
Invasive Weed Optimization Algorithm
KNN Classifier
Meta-heuristic Algorithms
Metaheuristic Algorithm
MIT BIH
MIT BIH Arrhythmia Database
MIT BIH Database
Multilevel Thresholding
SVM Classifier
swarm intelligence methods
truss structure design
UCI Dataset
Wilcoxon Rank Test

Product details

  • ISBN 9781032070919
  • Weight: 800g
  • Dimensions: 156 x 234mm
  • Publication Date: 20 Sep 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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.
About

Seyedali Mirjalili is a Professor at Torrens University Center for Artificial Intelligence Research and Optimization and internationally recognized for his advances in nature-inspired Artificial Intelligence (AI) techniques. He is the author of more than 300 publications including five books, 250 journal articles, 20 conference papers, and 30 book chapters. With more than 50,000 citations and H-index of 75, he is one of the most influential AI researchers in the world. From Google Scholar metrics, he is globally the most cited researcher in Optimization using AI techniques, which is his main area of expertise. Since 2019, he has been in the list of 1% highly-cited researchers and named as one of the most influential researchers in the world by Web of Science. In 2021, The Australian newspaper named him as the top researcher in Australia in three fields of Artificial Intelligence, Evolutionary Computation, and Fuzzy Systems. He is a senior member of IEEE and is serving as an editor of leading AI journals including Neurocomputing, Applied Soft Computing, Advances in Engineering Software, Computers in Biology and Medicine, Healthcare Analytics, and Applied Intelligence.