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.
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| Formato: | Electrónico eBook |
| Lenguaje: | inglés |
| Publicado: |
London ; San Diego, CA :
Academic Press, an imprint of Elsevier,
[2024]
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| Acceso en línea: | ScienceDirect |
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| 020 | |a 9780323953641 |q electronic book | ||
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| 082 | 0 | 4 | |a 519.6 |2 23/eng/20231226 |1 https://id.oclc.org/worldcat/ddc/E3Qg3jh8b9byKVyRcCDVXQGcgX |
| 245 | 0 | 0 | |a Handbook of Whale Optimization Algorithm : |b Variants, Hybrids, Improvements, and Applications / |c edited by Seyedali Mirjalili. |
| 264 | 1 | |a London ; |a San Diego, CA : |b Academic Press, an imprint of Elsevier, |c [2024] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on December 28, 2023). | ||
| 505 | 0 | |a 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). | |
| 505 | 8 | |a 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). | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 14.2 Literature review -- 14.2.1 Methods for breast cancer detection using BUSI database -- 14.2.2 Applications of WOA -- 14.3 Materials & -- 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Metaheuristics. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Computer algorithms. | |
| 650 | 6 | |a Optimisation mathématique. |0 (CaQQLa)201-0007680 | |
| 650 | 6 | |a Algorithmes. |0 (CaQQLa)201-0001230 | |
| 650 | 7 | |a algorithms. |2 aat |0 (CStmoGRI)aat300065585 | |
| 650 | 7 | |a Computer algorithms. |2 fast |0 (OCoLC)fst00872010 | |
| 650 | 7 | |a Mathematical optimization. |2 fast |0 (OCoLC)fst01012099 | |
| 650 | 7 | |a Metaheuristics. |2 fast |0 (OCoLC)fst02000551 | |
| 700 | 1 | |a Mirjalili, Seyedali, |e editor. | |
| 776 | 0 | 8 | |i Print version: |z 0323953654 |z 9780323953658 |w (OCoLC)1348636369 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780323953658 |