Handbook of Whale Optimization Algorithm

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. Whale Optimization Algorithm has been cited more than 5000 times in Google Scholar, thus solving optimization problems 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 to name a few. This handbook provides readers with in-depth analysis of this algorithm and existing methods in the literature to cope with such challenges.

The authors and editors also propose several improvements, variants and hybrids of this algorithm. Several applications are also covered to demonstrate the applicability of methods in this book.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Preface
Chapter 1: Presenting appointment scheduling with considering whale optimization algorithm in healthcare management
Abstract
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
Chapter 2: Recent advances of whale optimization algorithm, its versions and applications
Abstract
Acknowledgement
2.1. Introduction
2.2. The growth of whale optimizer algorithm
2.3. Fundamentals to whale optimizer algorithm
2.4. Variants of WOA algorithm
2.5. Applications of whale optimizer algorithm
2.6. Open source software of whale optimizer algorithm
2.7. Conclusions
Conflict of interest
References
Chapter 3: A hybrid whale optimization algorithm with tabu search algorithm for resource allocation in indoor VLC systems
Abstract
3.1. Introduction
3.2. System model
3.3. Problem formulation
3.4. Preliminaries
3.5. Numerical results
3.6. Conclusion
References
Chapter 4: Use of whale optimization algorithm and its variants for cloud task scheduling: a review
Abstract
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
4.7. Discussions
4.8. Conclusion and future work
Declaration of competing interest
References
Chapter 5: Whale optimization algorithm and its application in machine learning
Abstract
5.1. Introduction
5.2. Whale optimization algorithm
5.3. WOA for various machine learning tasks
5.4. Discussion
5.5. Conclusion and future direction
References
Chapter 6: Whale optimization algorithm - comprehensive meta analysis on hybridization, latest improvements, variants and applications for complex optimization problems
Abstract
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
Chapter 7: Near-fault ground motion attenuation of large-scale steel structure by upgraded whale optimization algorithm
Abstract
7.1. Introduction
7.2. Fuzzy logic controller (FLC)
7.3. Optimization algorithms
7.4. Design example
7.5. Statement of the optimization problem
7.6. Numerical results
7.7. Conclusion
References
Chapter 8: SDN-based optimal task scheduling method in Fog-IoT network using combination of AO and WOA
Abstract
8.1. Introduction
8.2. Related works
8.3. Problem formulation
8.4. Prerequisites
8.5. A proposed TSch method using SDN-based AWOA
8.6. Evaluation metrics and experimental results
8.7. Conclusion and future work
References
Chapter 9: An enhanced whale optimization algorithm using the Nelder-Mead algorithm and logistic chaotic map
Abstract
9.1. Introduction
9.2. Related work
9.3. Overview of used algorithms
9.4. Proposed algorithm
9.5. Experimental results and discussion
9.6. Conclusion and future scope
References
Chapter 10: Multi-criterion design optimization of contamination detection sensors in water distribution systems
Abstract
10.1. Introduction
10.2. Problem statement
10.3. Comparing metrics
10.4. Case study
10.5. Results and discussion
Conclusion
References
Chapter 11: Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis
Abstract
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
Chapter 12: Equitable and fair performance evaluation of whale optimization algorithm
Abstract
12.1. Introduction
12.2. Background
12.3. Evaluation
12.4. Result evaluation
12.5. Summary
References
Chapter 13: Multi-objective archived-based whale optimization algorithm
Abstract
13.1. Introduction
13.2. Whale optimization algorithm
13.3. Multi-objective whale optimization algorithm
13.4. Simulation and results
13.5. Conclusion
References
Chapter 14: U-WOA: an unsupervised whale optimization algorithm based deep feature selection method for cancer detection in breast ultrasound images
Abstract
14.1. Introduction
14.2. Literature review
14.3. Materials & methods
14.4. Results
14.5. Conclusion
References
Chapter 15: Constraint optimization: solving engineering design problems using Whale Optimization Algorithm (WOA)
Abstract
15.1. Introduction
15.2. Related work
15.3. Whale optimization algorithm
15.4. Engineering design problems
15.5. Conclusion
Appendix 15.A.
References
Chapter 16: F-WOA: an improved whale optimization algorithm based on Fibonacci search principle for global optimization
Abstract
16.1. Introduction
16.2. Literature review
16.3. Whale optimization algorithm (WOA)
16.4. Proposed F-WOA algorithm
16.5. Simulation study and analysis
16.6. Engineering design problems
16.7. Conclusions and future extensions
Compliance with ethical standards
Appendix 16.A. Formulation of 14 benchmark functions
Appendix 16.B. Tension/compression spring design problem
Appendix 16.C. Cantilever beam design problem
References
Chapter 17: A random weight and random best solution based improved whale optimization algorithm for optimization issues
Abstract
17.1. Introduction
17.2. Whale optimization algorithm
17.3. Proposed RWbWOA
17.4. Discussion of numerical results
17.5. Conclusion
Appendix 17.A.
References
Chapter 18: Guided whale optimization algorithm (guided WOA) with its application
Abstract
18.1. Introduction
18.2. Whale optimization algorithm
18.3. Guided WOA
18.4. Binary guided WOA algorithm
18.5. Guided WOA applications
18.6. Conclusion
References
Chapter 19: Optimal Power Flow with renewable power generations using hyper-heuristic technique
Abstract
Acknowledgement
19.1. Introduction
19.2. Optimal Power Flow incorporating stochastic solar, wind, and small hydro power generation
19.3. Metaheuristic algorithms as LLH
19.4. Hyper heuristic strategies for OPF solution
19.5. Implementation of HH into OPF solution
19.6. Results and discussion
19.7. Conclusion
References
Chapter 20: An efficient single image dehazing algorithm based on patch-wise transmission map estimation using Whale Optimization Algorithm
Abstract
20.1. Introduction
20.2. Whale optimization algorithm
20.3. Proposed method
20.4. Experimental results
20.5. Conclusion
References
Chapter 21: An enhanced whale optimization algorithm with opposition-based learning for LEDs placement in indoor VLC systems
Abstract
21.1. Introduction
21.2. LEDs placement problem formulation
21.3. Preliminaries
21.4. The proposed EWOA for solving the LEDs placement problem
21.5. Experimental results and discussions
21.6. Conclusion
References
Chapter 22: Adaptive bi-level whale optimization algorithm for maximizing the power output of hybrid wave-wind energy site
Abstract
22.1. Introduction
22.2. System description and modeling
22.3. Optimization setup
22.4. Meta-heuristic optimization algorithms
22.5. Numerical results and discussions
22.6. Conclusions
References
Chapter 23: Sizing optimization of truss structures using hybrid whale optimization algorithm
Abstract
23.1. Introduction
23.2. Truss structure problem
23.3. Whale optimization algorithm (WOA)
23.4. Adaptive β hill climbing (AβHC)
23.5. Hybridizing the WOA with AβHC
23.6. Experiments and results
23.7. Conclusion and future work
References
Chapter 24: Whale Optimization Algorithm (WOA) for BIM-based resource trade-off in construction project scheduling
Abstract
24.1. Introduction
24.2. Problem statement
24.3. Optimization results and discussion
24.4. Conclusion
References
Chapter 25: Applications of whale migration algorithm in optimal power flow problems of power systems
Abstract
25.1. Introduction
25.2. Problem formulation
25.3. Description of WMA
25.4. Simulation
25.5. Discussion
25.6. Conclusion
Declaration of competing interest
References
Chapter 26: Optimizing CNN architecture using whale optimization algorithm for lung cancer detection
Abstract
26.1. Introduction
26.2. Literature survey
26.3. Optimized convolutional neural network
26.4. Experimental results and discussion
26.5. Conclusion
References
Chapter 27: Multi-response optimization of plasma arc cutting on Monel 400 alloy through whale optimization algorithm
Abstract
27.1. Introduction
27.2. Methodologies
27.3. Experimental details
27.4. Results and discussion
27.5. Conclusions
References
Chapter 28: Hybrid whale optimization algorithm for enhancing K-means clustering technique
Abstract
28.1. Introduction
28.2. Related works
28.3. Hybrid whale optimization algorithm
28.4. Evaluation and discussion of the results
28.5. Conclusion and future work
References
Chapter 29: Whale optimization algorithm based controller design for air-fuel ratio system
Abstract
29.1. Introduction
29.2. Whale optimization algorithm
29.3. Problem definition and proposed design methodology
29.4. Simulation results
29.5. Conclusion
Appendix 29.A.
References
Chapter 30: Application of whale optimization algorithm to infinite impulse response system identification
Abstract
30.1. Introduction
30.2. Whale optimization algorithm
30.3. Problem formulation for IIR system identification
30.4. Simulation results
30.5. Conclusion
Declaration of competing interests
References
Chapter 31: Optimization of SHE problem with WOA in AC-AC choppers
Abstract
31.1. Introduction
31.2. PWM AC-AC chopper
31.3. Whale optimization algorithm (WOA)
31.4. Problem formulation and simulation result
31.5. Conclusion
References
Chapter 32: A WOA-based path planning approach for UAVs to avoid collisions in cluttered areas
Abstract
32.1. Introduction
32.2. The whale optimization algorithm
32.3. Dynamics of agents and constraints
32.4. Path planning
32.5. Simulation environment
32.6. Conclusion
32.7. Future works
References
Chapter 33: Application of an Improved Whale Optimization Algorithm for optimal design of shell and tube heat exchanger
Abstract
33.1. Introduction
33.2. Mechanism of Whale Optimization Algorithm (WOA)
33.3. Improved Whale Optimization Algorithm (IWOA)
33.4. Mathematical models of SHTE
33.5. Results and discussion
33.6. Conclusion
References
Chapter 34: Whale-optimized convolutional neural network for potato fungal pathogens disease classification
Abstract
34.1. Introduction
34.2. Fungal pathogens
34.3. Database
34.4. Artificial intelligence (AI)
34.5. Convolutional neural network
34.6. Whale optimization algorithm
34.7. Performance analysis
34.8. Challenges
34.9. Conclusion
References
Chapter 35: Whale optimization algorithm for scheduling and sequencing
Abstract
35.1. Introduction
35.2. Whale optimization algorithm (WOA)
35.3. Applications of WOA
35.4. Conclusion
References
Chapter 36: Tuning SVMs' hyperparameters using the whale optimization algorithm
Abstract
36.1. Introduction
36.2. Whale optimization algorithm and improved versions
36.3. SVM: a brief history and recent developments
36.4. SVMs: a general overview
36.5. Hyperparameter tuning
36.6. Empirical analysis of metaheuristic-based SVM training
36.7. Conclusion
References
Chapter 37: Gene selection for microarray data classification based on mutual information and binary whale optimization algorithm
Abstract
37.1. Introduction
37.2. Whale Optimization Algorithm (WOA)
37.3. Binary Whale Optimization Algorithm (BWOA)
37.4. Experimental results
37.5. Conclusion
References
Chapter 38: A new hybrid whale optimization algorithm and golden jackal optimization for data clustering
Abstract
38.1. Introduction
38.2. Related works
38.3. Fundamental research
38.4. Proposed model
38.5. Result and discussion
38.6. Conclusion and future works
References
Chapter 39: Feature selection based on dataset variance optimization using Whale Optimization Algorithm (WOA)
Abstract
39.1. Introduction
39.2. Related work
39.3. Method
39.4. Proposed approach
39.5. Experimentation
39.6. Results and comparative analysis
39.7. Conclusion and future work
References
Chapter 40: Whale optimization algorithm for Covid-19 detection based on ECG
Abstract
40.1. Introduction
40.2. Related work
40.3. Material and methods
40.4. Results and description
40.5. Conclusion
References
Chapter 41: Whale optimization algorithm for optimization of truss structures with multiple frequency constraints
Abstract
41.1. Introduction
41.2. Problems definition
41.3. Optimization benchmark with results
41.4. Conclusion and future work
References
Chapter 42: A novel version of whale optimization algorithm for solving optimization problems
Abstract
42.1. Introduction
42.2. Whale optimization algorithm (WOA)
42.3. Advanced whale optimization algorithm (AWOA)
42.4. Engineering problems
42.5. Conclusion and future work
References
Chapter 43: Binary whale optimization algorithm for topology planning in wireless mesh networks
Abstract
43.1. Introduction
43.2. Problem formulation
43.3. Whale optimization algorithm (WOA)
43.4. Binary whale optimization algorithm (BWOA)
43.5. Simulation results
43.6. Conclusion
References
Chapter 44: A survey of different Whale Optimization Algorithm applications in water engineering and management
Abstract
44.1. Application of WOA in lake water level (LWL) modeling
44.2. Application of WOA in pan evaporation estimation
44.3. Application of WOA in modeling reference evapotranspiration
44.4. Application of WOA in rainfall & runoff modeling estimation
44.5. Application of WOA in flood frequency analysis and daily water level
44.6. Application of WOA in groundwater level modeling
44.7. Application of WOA in reservoirs operation
44.8. List of abbreviations
References
Chapter 45: A MTIS method using a combined of whale and moth-flame optimization algorithms
Abstract
45.1. Introduction
45.2. Related work
45.3. Preliminaries
45.4. Proposed method
45.5. Performance analysis and test results
45.6. Conclusions
References
Index

Variants, Hybrids, Improvements, and Applications

Purchase options

Save 50% on book bundles

Institutional subscription on ScienceDirect

Seyedali Mirjalili