Handbook of Whale Optimization Algorithm : Variants, Hybrids, Improvements, and Applications /

Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides the most in-depth look at an emerging meta-heuristic that has been widely used in both science and industry.

Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Mirjalili, Seyedali (Editor)
Format:	eBook
Language:	English
Published:	London ; San Diego, CA : Academic Press, an imprint of Elsevier, [2024]
Subjects:	Metaheuristics. Mathematical optimization. Computer algorithms. Optimisation mathématique. Algorithmes. algorithms. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Handbook of Whale Optimization Algorithm
Copyright
Contents
List of contributors
Preface
1 Presenting appointment scheduling with considering whale optimization algorithm in healthcare management
1.1 Introduction
1.2 Whale optimization algorithm
1.3 Problem statement
1.4 Different method of WOA
1.5 Computational model
1.6 Solution approach
1.7 Results analysis and discussion
1.8 Conclusion and future directions
References
2 Recent advances of whale optimization algorithm, its versions and applications
2.1 Introduction
2.2 The growth of whale optimizer algorithm
2.2.1 No. publications per year
2.2.2 No. publications per publisher
2.2.3 No. publications per affiliation
2.2.4 No. publications per country
2.3 Fundamentals to whale optimizer algorithm
2.3.1 Inspiration of WOA
2.3.2 Procedure of WOA
2.3.2.1 Encircling prey
2.3.2.2 Bubble-net attacking method (exploitation phase)
2.3.2.3 Search for prey (exploration phase)
2.3.2.4 WOA optimization steps
2.4 Variants of WOA algorithm
2.4.1 Original versions of WOA
2.4.2 Modified versions of WOA
2.4.2.1 Binary WOA
2.4.3 Multi-objective WOA
2.4.4 Hybridized versions of WOA
2.4.4.1 WOA with other algorithms
2.5 Applications of whale optimizer algorithm
2.6 Open source software of whale optimizer algorithm
2.7 Conclusions
Conflict of interest
Acknowledgment
References
3 A hybrid whale optimization algorithm with tabu search algorithm for resource allocation in indoor VLC systems
3.1 Introduction
3.2 System model
3.3 Problem formulation
3.4 Preliminaries
3.4.1 Whale Optimization Algorithm (WOA)
3.4.1.1 Exploitation
3.4.1.1.1 Encircling the prey
3.4.1.1.2 Spiral updating mechanism
3.4.1.2 Exploration phase
3.4.2 Tabu search algorithm (TS).
3.4.3 Hybrid algorithm (WOATS)
3.4.4 Time complexity of the proposed WOATS algorithm
3.5 Numerical results
3.5.1 Impact of changing the user count
3.5.2 Impact of changing the number of activated PDs
3.5.3 Convergence analysis
3.6 Conclusion
References
4 Use of whale optimization algorithm and its variants for cloud task scheduling: a review
4.1 Introduction
4.2 Objective of scheduling
4.3 Research methodology
4.4 Meta-heuristic scheduling methods
4.5 WOA algorithm
4.6 Types of whale optimization-based scheduling
4.6.1 Standard WOA
4.6.2 Multi-objective WOA
4.6.3 Improved WOA
4.6.4 Hybrid WOA
4.7 Discussions
4.8 Conclusion and future work
Declaration of competing interest
References
5 Whale optimization algorithm and its application in machine learning
5.1 Introduction
5.2 Whale optimization algorithm
5.3 WOA for various machine learning tasks
5.3.1 Feature selection
5.3.1.1 Standard representation
5.3.1.2 Binary representation
5.3.2 WOA for data clustering
5.3.3 WOA for data classification
5.3.4 WOA for neural network and deep neural network training
5.3.4.1 Artificial neural network weights and parameters tuning
5.3.4.2 Hyperparameters tuning for deep neural networks
5.4 Discussion
5.5 Conclusion and future direction
References
6 Whale optimization algorithm
comprehensive meta analysis on hybridization, latest improvements, variants and application...
6.1 Introduction
6.2 Whale optimization algorithm
6.3 Research methodology
6.4 Literature review
6.5 Existing problems, applications, and future research avenues
6.6 Conclusion
References
7 Near-fault ground motion attenuation of large-scale steel structure by upgraded whale optimization algorithm
7.1 Introduction
7.2 Fuzzy logic controller (FLC).
7.3 Optimization algorithms
7.3.1 Whale optimization algorithm (WOA)
7.3.2 Upgraded WOA (UWOA)
7.4 Design example
7.4.1 Near-fault ground motion
7.4.2 FLC implementation
7.4.3 Performance criteria
7.5 Statement of the optimization problem
7.6 Numerical results
7.7 Conclusion
References
8 SDN-based optimal task scheduling method in Fog-IoT network using combination of AO and WOA
8.1 Introduction
8.2 Related works
8.2.1 Non-SDN based TSch algorithms
8.2.2 SDN-based TSch algorithms
8.3 Problem formulation
8.4 Prerequisites
8.4.1 Software-defined networking
8.4.2 Firefly algorithm
8.4.3 Harris Hawks algorithm
8.4.3.1 In the ExploRation phase
8.4.3.2 In the ExploItation phase
8.4.4 Partial swarm algorithm
8.4.5 Aquila optimizer algorithm
8.4.6 Whale optimization algorithm
8.5 A proposed TSch method using SDN-based AWOA
8.5.1 Initialization phase
8.5.2 Updating the solutions phase
8.5.3 Computational complexity of AO, WOA, and AWOA
8.5.4 Proposed SDN based framework
8.5.5 The limitation of the proposed hybrid AO and WOA
8.6 Evaluation metrics and experimental results
8.6.1 Evaluation metrics
8.6.1.1 Computational complexity
8.6.1.2 Objective function value
8.6.1.3 Makespan time
8.6.1.4 Throughput time
8.6.1.5 Performance improvement rate
8.6.1.6 Resource consumption of modules designed in SDN-based framework
8.6.1.7 Transmission delay in SDN-based framework
8.6.2 Results of comparison with existing works
8.7 Conclusion and future work
References
9 An enhanced whale optimization algorithm using the Nelder-Mead algorithm and logistic chaotic map
9.1 Introduction
9.2 Related work
9.3 Overview of used algorithms
9.3.1 Whale optimization
9.3.1.1 Encircling prey phase
9.3.1.2 Bubble-net attacking phase.
9.3.1.3 Search for prey phase
9.3.2 Nelder-Mead method
9.3.3 Logistic chaotic map
9.4 Proposed algorithm
9.5 Experimental results and discussion
9.6 Conclusion and future scope
References
10 Multi-criterion design optimization of contamination detection sensors in water distribution systems
10.1 Introduction
10.2 Problem statement
10.2.1 Objective functions
10.2.1.1 Simulation model
10.2.2 Whale optimization algorithm (WOA)
10.2.2.1 Encircling prey
10.2.2.2 Exploitation phase (bubble-net attacking)
10.2.2.3 Exploration phase (search for prey)
10.3 Comparing metrics
10.4 Case study
10.5 Results and discussion
Conclusion
References
11 Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis
11.1 Introduction
11.2 Exploration-exploitation tradeoffs in WOA
11.3 Dimension-wise diversity measurement
11.4 Results and analysis
11.5 Summary
References
12 Equitable and fair performance evaluation of whale optimization algorithm
12.1 Introduction
12.2 Background
12.2.1 WOA
12.2.2 BSA
12.2.3 FDO
12.2.4 PSO
12.2.5 FF
12.3 Evaluation
12.3.1 Evaluation method
12.3.2 Problems and initial parameters
12.3.3 Statistical analysis and tool
12.4 Result evaluation
12.4.1 Results of three evaluations
12.4.2 Computational cost
12.4.3 Convergence analysis
12.5 Summary
References
13 Multi-objective archived-based whale optimization algorithm
13.1 Introduction
13.2 Whale optimization algorithm
13.3 Multi-objective whale optimization algorithm
13.4 Simulation and results
13.5 Conclusion
References
14 U-WOA: an unsupervised whale optimization algorithm based deep feature selection method for cancer detection in breast ul...
14.1 Introduction.
14.2 Literature review
14.2.1 Methods for breast cancer detection using BUSI database
14.2.2 Applications of WOA
14.3 Materials &amp
methods
14.3.1 Dataset description
14.3.2 Whale optimization algorithm
14.3.2.1 Encircling prey
14.3.2.2 Bubble-net attacking method (exploitation phase)
14.3.2.3 Search for prey (exploration phase)
14.3.3 Unsupervised WOA (U-WOA)
14.3.3.1 Pearson's correlation coefficient (PCC)
14.3.3.2 Spearman correlation coefficient (SCC)
14.3.3.3 ReliefF
14.3.4 Methodology
14.3.4.1 Deep feature extraction
14.3.4.2 Feature selection using U-WOA
14.3.4.3 Transfer function
14.3.4.4 Fitness function
14.3.4.5 Classification
14.4 Results
14.4.1 Performance metrics
14.4.2 Hyperparameters for TL models
14.4.3 Results and discussion
14.5 Conclusion
References
15 Constraint optimization: solving engineering design problems using Whale Optimization Algorithm (WOA)
15.1 Introduction
15.1.1 Meta heuristic techniques
15.2 Related work
15.3 Whale optimization algorithm
15.3.1 Inspiration
15.3.2 Mathematical model
15.3.2.1 Multi-agent navigation
15.3.2.2 Exploitation phase
15.3.2.3 Exploration phase
15.4 Engineering design problems
15.4.1 Helical spring (FM1)
15.4.2 Tension/compression spring (FM2)
15.4.3 Welded beam design (FM3)
15.4.4 Gear train design (FM4)
15.4.5 Pressure vessel design (FM5)
15.4.6 Three truss design (FM6)
15.4.7 Tubular column design (FM7)
15.4.8 Hydrodynamic thrust bearing design (FM8)
15.4.9 Spur gear design (FM9)
15.4.10 Step cone pulley design (FM10)
15.4.11 Reinforced concrete beam design (FM11)
15.4.12 Piston lever design (FM12)
15.4.13 Comparative analysis
15.4.14 Computational complexity analysis
15.5 Conclusion
Appendix 15.A
References.