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

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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.

Author(s): Seyedali Mirjalili
Series: Advances in Metaheuristics
Publisher: CRC Press
Year: 2022

Language: English
Pages: 332
City: Boca Raton

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Editor
Contributors
SECTION I Moth-Flame Optimization Algorithm for Different Optimization Problems
CHAPTER 1 ◾ Optimization and Metaheuristics
1.1 Introduction to Optimization
1.1.1 Derivative-Based Optimization Algorithms
1.1.2 Non-Derivative-Based Optimization Algorithms
1.1.3 Metaheuristics
Reference
CHAPTER 2 ◾ Moth-Flame Optimization Algorithm for
Feature Selection: A Review and Future Trends
2.1 Introduction
2.2 Feature Selection
2.3 MFO Algorithm
2.3.1 MFO Inspiration
2.3.2 MFO’s Mathematical Model
2.4 Feature Selection Using MFO Algorithm
2.5 Existing Studies Based on MFO Feature Selection
2.6 Discussion and Future Directions
2.7 Conclusion
References
CHAPTER 3 ◾ An Efficient Binary Moth-Flame Optimization
Algorithm with Cauchy Mutation for Solving
the Graph Coloring Problem
3.1 Introduction
3.2 Graph Coloring Problem Formulation
3.3 Moth-Flame Optimization Algorithm and Binary Moth-Flame Optimization Algorithm
3.3.1 Moth-Flame Optimization (MFO) Algorithm
3.3.2 Binary Moth-Flame Optimization Algorithm
3.4 Efficient BMFO Algorithm With Cauchy Mutation for Solving
the GCP
3.4.1 Efficient BMFO Algorithm With Cauchy Mutation
3.4.2 Representation of the Solution
3.4.3 Objective Function
3.5 Simulation Results
3.6 Conclusion
References
CHAPTER 4 ◾ Evolving Deep Neural Network by
Customized Moth-Flame Optimization
Algorithm for Underwater Targets Recognition
4.1 Introduction
4.2 Methods
4.2.1 Deep Neural Network (DNN) Architectures
4.2.2 Convolutional Neural Networks
4.2.3 Sonar Dataset
4.2.4 MFO Algorithm
4.3 Methodology
4.3.1 Customized MFO
4.3.2 Presentation of Searching Agents
4.3.3 Loss Function
4.4 Simulation Results and Discussion
4.4.1 The Analysis of Time Complexity
4.4.2 Sensitivity Analysis of Designed Model
4.5 Conclusions
References
SECTION II Variants of Moth-Flame Optimization Algorithm
CHAPTER 5 ◾ Multi-objective Moth-Flame Optimization
Algorithm for Engineering Problems
5.1 Introduction
5.2 Related Works
5.3 MMFO Algorithm
5.4 Results and Discussion
5.4.1 Performance Metrics
5.4.1.1 Generational Distance (GD)
5.4.1.2 Spacing (S)
5.4.1.3 Maximum Spread (MS)
5.4.1.4 Inverted Generational Distance (IGD)
5.5 Discussion of the Engineering Design Functions
5.6 Conclusion and Future Directions
References
CHAPTER 6 ◾ Accelerating Optimization Using Vectorized
Moth-Flame Optimizer (vMFO)
6.1 Introduction
6.2 MFO Algorithm and the Vanilla Implementation
6.3 Vectorized MFO
6.4 Experiments
6.5 Conclusion
References
CHAPTER 7 ◾ A Modified Moth-Flame Optimization
Algorithm for Image Segmentation
7.1 Introduction
7.2 Problem formulation of multilevel thresholding
7.2.1 Kapur’s entropy method
7.2.2 Segmentation performance measure
7.3 Moth-flame optimization
7.4 Proposed modified m-MFO algorithm
7.5 Comparison of numerical results
7.5.1 Comparison with evaluated benchmark function results
7.5.2 Comparison of Image segmentation results
7.6 Convergence analysis
7.7 Conclusion
References
CHAPTER 8 ◾ Moth-Flame Optimization-Based Deep
Feature Selection for Cardiovascular Disease
Detection Using ECG Signal
8.1 Introduction
8.2 Related Work
8.3 Motivation
8.4 Dataset Used
8.4.1 Pre-Processing
8.5 Methodology
8.5.1 VGG16: A Brief Overview
8.5.2 Moth-Flame Optimization
8.6 Experimental Results and Discussion
8.7 Conclusion
References
SECTION III Hybrids and Improvements of Moth-Flame Optimization Algorithm
CHAPTER 9 ◾ Hybrid Moth-Flame Optimization Algorithm with Slime Mold Algorithm for Global Optimization
9.1 Introduction
9.2 Overview of Component Algorithms
9.2.1 MFO Algorithm
9.2.2 Overview of SMA
9.3 The Proposed hMFOSMA
9.3.1 Motivation of the Work
9.3.2 Framework of hMFOSMA
9.4 Performance Results and Analysis
9.4.1 Comparison of the Proposed hMFOSMA With
Other Algorithm
9.4.2 Convergence Analysis
9.4.3 Statistical Analysis
9.5 Application of hMFOSMA on Engineering Problems
9.5.1 EP.1: Gas Transmission Compressor Design Problem
9.5.2 EP.2: Optimal Capacity of Gas Production Facilities
9.6 Conclusion and Future Directions
References
CHAPTER 10 ◾ Hybrid Aquila Optimizer with Moth-Flame Optimization Algorithm for Global Optimization
10.1 Introduction
10.2 The Proposed Hybrid Aquila Optimizer with Moth-Flame Optimization Algorithm (AOMFO)
10.2.1 Aquila Optimizer (AO)
10.2.1.1 Expanded Exploration
10.2.1.2 Narrowed Exploration
10.2.1.3 Expanded Exploitation
10.2.1.4 Narrowed Exploitation
10.2.2 Moth-Flame Optimization (MFO) Algorithm
10.2.2.1 Generate the Initial Population of MFO
10.2.2.2 Updating the Positions
10.2.2.3 Updating the Number of Flames
10.2.3 The Proposed AOMFO Method
10.2.3.1 Solutions Initialization
10.2.3.2 Structure of the Proposed AOMFO
10.3 Experiments and Results
10.3.1 Parameter Settings
10.3.2 Description of the Tested Benchmark Functions
10.3.3 Results and Discussions
10.4 Conclusion and Promising Potential Future Works
References
CHAPTER 11 ◾ Boosting Moth-Flame Optimization
Algorithm by Arithmetic Optimization
Algorithm for Data Clustering
11.1 Introduction
11.2 DC Application
11.3 The Proposed Clustering Algorithm (MFOAOA)
11.3.1 MFO Algorithm
11.3.1.1 Generate the Initial Population of MFO
11.3.1.2 Updating the Positions
11.3.1.3 Updating the Number of Flames
11.3.2 Arithmetic Optimization Algorithm (AOA)
11.3.3 Exploration Phase
11.3.4 Exploitation Phase
11.3.5 The Proposed Hybrid Moth-Flame Optimization with
Arithmetic Optimization Algorithm (MFOAOA)
11.3.5.1 Solutions Initialization
11.3.5.2 Structure of the Proposed MFOAOA
11.4 Experiments and Discussion
11.4.1 Experiments Settings
11.4.2 Datasets Description
11.4.3 Results
11.5 Conclusion and Future Works
References
SECTION IV Applications of Moth-Flame
Optimization Algorithm
CHAPTER 12 ◾ Moth-Flame Optimization Algorithm,
Arithmetic Optimization Algorithm, Aquila
Optimizer, Gray Wolf Optimizer, and Sine
Cosine Algorithm: A Comparative Analysis
Using Multilevel Thresholding Image
Segmentation Problems
12.1 Introduction
12.2 Problem Definitions
12.3 Experiments and Discussion
12.3.1 Experiments Details
12.3.2 Results
12.4 Conclusion and Future Works
References
CHAPTER 13 ◾ Optimal Design of Truss Structures with
Continuous Variable Using Moth-Flame
Optimization
13.1 Introduction
13.2 Problem Definition
13.3 Numerical Examples
13.3.1 The 25-bar Space Truss
13.3.2 The 72-bar Space Truss
13.3.3 The 200-bar Planar Truss
References
CHAPTER 14 ◾ Deep Feature Selection Using Moth-Flame Optimization for Facial Expression Recognition from Thermal Images
14.1 Introduction
14.2 Literature Survey
14.3 Database Used
14.4 Methods and Materials
14.4.1 CNN Model Used
14.4.1.1 Residual Unit
14.4.1.2 Transformation Unit
14.4.2 Feature Extraction
14.4.3 Feature Selection Using MFO
14.5 Results and Discussion
14.6 Conclusion
References
CHAPTER 15 ◾ Design Optimization of Photonic
Crystal Filter Using Moth-Flame
Optimization Algorithm
15.1 Introduction
15.2 PhCs and PhC Filters
15.3 Design Optimization of PhC Filter by Using MFO
15.4 Results and Discussion
References
INDEX