Metaheuristic Optimization Algorithms. Optimizers, Analysis, and Applications

✍ Scribed by Laith Abualigah

• Publisher: Morgan Kaufmann, Elsevier
• Year: 2024
• Tongue: English
• Leaves: 291
• Category: Library

✦ Table of Contents

Front Cover
Metaheuristic Optimization Algorithms
Copyright Page
Contents
List of contributors
1 Particle swarm optimization algorithm: review and applications
1.1 Introduction
1.2 Particle swarm optimization
1.2.1 Standard particle swarm optimization
1.2.2 Particle swarm optimization algorithm
1.3 Related works
1.3.1 Neural networks
1.3.2 Feature selection
1.3.3 Data clustering
1.3.4 Mobile robots
1.4 Discussion
1.5 Conclusion
References
2 Social spider optimization algorithm: survey and new applications
2.1 Introduction
2.2 Related work
2.2.1 Medical field
2.2.2 Engineering field
2.2.3 Mathematics field
2.2.4 Artificial intelligence field
2.2.5 Data science
2.3 Social spider optimization method
2.4 Experiment result
2.5 Discussion
2.6 Conclusion
References
3 Animal migration optimization algorithm: novel optimizer, analysis, and applications
3.1 Introduction
3.2 Animal migration optimization algorithm procedure
3.3 Related works
3.3.1 Image processing
3.3.2 Data clustering
3.3.3 Data mining
3.3.4 Benchmark functions
3.3.5 Computer networks
3.3.6 Neural networks
3.3.7 Other applications
3.4 Discussion
3.5 Conclusion
References
4 A Survey of cuckoo search algorithm: optimizer and new applications
4.1 Introduction
4.2 Cuckoo search algorithm
4.2.1 Cuckoo rearing conduct
4.2.2 Lévy trips in nature
4.3 Related works
4.4 Method
4.5 Discussion
4.6 Advanced work
4.7 Conclusion
References
5 Teaching–learning-based optimization algorithm: analysis study and its application
5.1 Introduction
5.2 Teaching–learning-based optimization
5.2.1 Teacher section
5.2.2 Learner section
5.3 Literature review
5.3.1 Optimization problem
5.3.2 Technoeconomic analysis
5.3.3 Analytical process
5.3.4 Global optimization
5.3.5 Medical disease diagnosis
5.3.6 Data clustering
5.3.7 Shape and size optimization
5.3.8 Investment decisions
5.3.9 Large graph coloring problems
5.4 Discussion and future works
5.5 Conclusion
References
6 Arithmetic optimization algorithm: a review and analysis
6.1 Introduction
6.2 Arithmetic optimization algorithm
6.2.1 Initialization
6.2.2 Exploration
6.2.3 Exploitation
6.3 Related Works
6.3.1 Engineering application
6.3.2 Artificial intelligence
6.3.3 Chemistry
6.3.4 Machine learning
6.3.5 Network
6.3.6 Other applications
6.4 Discussion
6.5 Conclusion and future work
References
7 Aquila optimizer: review, results and applications
7.1 Introduction
7.2 Procedure
7.2.1 Step1: (X1)
7.2.2 Step 2: (X2)
7.2.3 Step 3: (X3)
7.2.4 Step 4: (X4)
7.2.5 Aquila Optimizer Pseudocode
7.3 Related works
7.4 Discussion
7.5 Conclusion
References
8 Whale optimization algorithm: analysis and full survey
8.1 Introduction
8.2 The whale optimization algorithm
8.2.1 Inspiration
8.2.2 Mathematical model and the optimization algorithm
8.2.2.1 Encircling prey
8.2.2.2 Bubble-net attacking method
8.2.2.3 Exploration phase: searching for a prey
8.3 Related work
8.3.1 Computer networks
8.3.2 Network security
8.3.3 Clustering
8.3.4 Image processing
8.3.5 Feature selection
8.3.6 Electrical power and energy systems
8.4 Discussion
8.5 Conclusion and future work
References
9 Spider monkey optimizations: application review and results
9.1 Introduction
9.2 Spider monkey optimization algorithm
9.2.1 The behavior of spider monkey optimization
9.2.2 The spider monkey optimization algorithm
9.2.2.1 Preparation of the community
9.2.2.2 Second leader stage
9.2.2.3 First leader stage
9.2.2.4 First leader learning
9.2.2.5 Second leader learning
9.2.2.6 Second leader decision
9.2.2.7 First leader decision
9.2.3 Control parameters in spider monkey optimization
9.3 Related work
9.3.1 Optimization problems
9.3.2 Deep learning
9.3.3 Data clustering
9.3.4 Big data problems
9.3.5 Networking problems
9.3.6 Cloud computing
9.3.7 Scheduling issues
9.3.8 Privacy problems
9.3.9 Image processing
9.3.10 Software engineering field
9.3.11 Other applications
9.4 Discussion
9.5 Conclusion and future works
References
10 Marine predator’s algorithm: a survey of recent applications
10.1 Introduction
10.2 Marine Predator's Algorithm
10.3 Related Works
10.3.1 Engineering Problems
10.3.2 Image Processing
10.3.3 Benchmark Function
10.3.4 Feature Selection
10.4 Discussion
10.5 Conclusion and Future Work
References
11 Quantum approximate optimization algorithm: a review study and problems
11.1 Introduction
11.2 Methods
11.2.1 Fixed p algorithm
11.2.2 Concentration
11.2.3 The ring of disagrees
11.2.4 Maxcut on 3-regular graphs
11.2.5 Relation to the quantum adiabatic algorithm
11.2.6 A variant of the algorithm
11.3 Related works
11.4 Result
11.5 Discussion
11.6 Conclusion
References
12 Crow search algorithm: a survey of novel optimizer and its recent applications
12.1 Introduction
12.2 Crow search algorithm
12.2.1 Inspiration
12.2.2 Continuous crow search algorithm
12.3 Related work
12.4 Conclusion and future work
References
13 A review of Henry gas solubility optimization algorithm: a robust optimizer and applications
13.1 Introduction
13.2 Henry gas solubility optimization
13.2.1 Henry’s law
13.2.2 Inspiration source
13.2.3 Henry gas solubility optimization mathematical model
13.2.4 Exploration and exploitation phases
13.3 Related works
13.3.1 Data mining
13.3.2 Genome biology (motif discovery problems)
13.3.3 Engineering problems
13.3.3.1 Solar energy
13.3.3.2 Cloud computing task scheduling
13.3.4 Benchmark functions
13.3.5 Automatic voltage regulator
13.3.6 Optimization tasks
13.3.7 Prediction of soil shear force
13.3.8 Autonomous vehicle management system
13.3.9 Software engineering problems
13.3.10 Machine learning
13.3.11 Image processing
13.3.12 Optimal power system
13.4 Discussion
13.5 Conclusion and future works
References
14 A survey of the manta ray foraging optimization algorithm
14.1 Introduction
14.2 Manta ray foraging optimization
14.2.1 Chain foraging
14.2.2 Cyclone foraging
14.2.3 Somersault foraging
14.3 Related works
14.3.1 Machine learning
14.3.2 Engineering application
14.3.3 Network problems
14.3.4 Optimization problem
14.3.5 Image processing
14.3.6 Other applications
14.4 Discussion
14.5 Conclusion and future work
References
15 A review of mothflame optimization algorithm: analysis and applications
15.1 Introduction
15.2 Moth Flame Optimization Algorithm
15.2.1 Origin
15.2.2 Moth Flame Optimization Algorithm
15.2.3 Establishing a Moth Population
15.2.4 Updating the Moths’ Positions
15.3 The Growth of the Moth Flame Optimization Algorithm in the Literature
15.3.1 Variants
15.4 Application
15.4.1 Benchmark Functions
15.4.2 Chemical Applications
15.4.3 Economical Applications
15.4.4 Image Processing
15.4.5 Medical Applications
15.4.5.1 Breast Cancer Detection
15.4.5.2 Alzheimer’s Disease Diagnosis
15.4.6 Machine Learning
15.5 Discussion
15.6 Concluding Remarks
References
16 Gradient-based optimizer: analysis and application of the Berry software product
16.1 Introduction
16.2 Literature review
16.2.1 Gradient-based optimization
16.2.1.1 Theoretical background
16.2.1.2 Gradient-based optimization
16.2.1.2.1 Initialization
16.2.1.2.2 Gradient search rule
16.3 Results and discussion
16.4 Conclusion
References
17 A review of krill herd algorithm: optimization and its applications
17.1 Introduction
17.2 Krill herd algorithm procedure
17.2.1 Krill swarms herding behavior
17.2.2 Standard of krill herd
17.2.2.1 Movement induced by other instances (Krill)
17.2.2.2 Foraging activity
17.2.3 Krill herd algorithm
17.3 Related work
17.4 Conclusion
References
18 Salp swarm algorithm: survey, analysis, and new applications
18.1 Introduction
18.2 Related work procedure of the algorithm
18.2.1 Single-objective optimization problems
18.2.2 Single-objective optimization procedures
18.2.3 Multiobjective optimization problems
18.2.4 Multiobjective optimization procedures
18.2.5 Research and studies related to the subject of the study
18.3 Methods
18.3.1 Stimulation
18.3.2 Mathematical model
18.3.3 Single-objective SALP swarm algorithm
18.3.4 Multiobjective SALP Swarm algorithm
18.4 Results
18.4.1 Qualitative results of SALP swarm algorithm and discussion
18.4.2 Quantitative results of SALP swarm algorithm and discussion
18.4.3 On the CEC-BBOB-2015 test functions, SALP swarm algorithm, and harmony search
18.4.4 Scalability analysis
18.4.5 Results of multipurpose SALP swarm algorithm and discussion
18.5 Conclusion
References
Index
Back Cover