Metaheuristic Optimization Algorithms : Optimizers, Analysis, and Applications /
Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research fields. Metaheuristic Optimization Algorithms have become indispensable tools, with applica...
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| Formato: | Recurso Electrónico livro electrónico |
| Idioma: | inglês |
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Cambridge, MA :
Morgan Kaufmann, an imprint of Elsevier,
[2024]
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| Acesso em linha: | ScienceDirect |
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| 020 | |a 0443139261 |q electronic book | ||
| 020 | |a 9780443139260 |q (electronic bk.) | ||
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| 082 | 0 | 4 | |a 006.3 |2 23/eng/20240514 |1 https://id.oclc.org/worldcat/ddc/E3dqg9fJWMHBbdDv8FDmDb7k7p |
| 245 | 0 | 0 | |a Metaheuristic Optimization Algorithms : |b Optimizers, Analysis, and Applications / |c edited by Laith Abualigah. |
| 264 | 1 | |a Cambridge, MA : |b Morgan Kaufmann, 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. | ||
| 520 | |a Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research fields. Metaheuristic Optimization Algorithms have become indispensable tools, with applications in data analysis, text mining, classification problems, computer vision, image analysis, pattern recognition, medicine, and many others. Most complex systems problems involve a continuous flow of data that makes it impossible to manage and analyze manually. The outcome depends on the processing of high-dimensional data, most of it irregular and unordered, present in various forms such as text, images, videos, audio, and graphics. The authors of Meta-Heuristic Optimization Algorithms provide readers with a comprehensive overview of eighteen optimization algorithms to address this complex data, including Particle Swarm Optimization Algorithm, Arithmetic Optimization Algorithm, Whale Optimization Algorithm, and Marine Predators Algorithm, along with new and emerging methods such as Aquila Optimizer, Quantum Approximate Optimization Algorithm, Manta-Ray Foraging Optimization Algorithm, and Gradient Based Optimizer, among others. Each chapter includes an introduction to the modeling concepts used to create the algorithm, followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies to demonstrate how each algorithm can be applied to a variety of scientific and engineering solutions. World-renowned researchers and practitioners in Metaheuristics present the procedures and pseudocode for creating a wide range of optimization algorithms Helps readers formulate and design the best optimization algorithms for their research goals through case studies in a variety of real-world applications Helps readers understand the links between Metaheuristic algorithms and their application in Computational Intelligence, Machine Learning, and Deep Learning problems. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on July 12, 2024). | ||
| 505 | 0 | |a 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 Ľvy 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 650 | 0 | |a Metaheuristics. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 6 | |a Métaheuristiques. |0 (CaQQLa)201-0313163 | |
| 650 | 6 | |a Intelligence informatique. |0 (CaQQLa)201-0265216 | |
| 650 | 6 | |a Intelligence artificielle. |0 (CaQQLa)201-0008626 | |
| 650 | 7 | |a artificial intelligence. |2 aat |0 (CStmoGRI)aat300251574 | |
| 650 | 7 | |a Artificial intelligence |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Computational intelligence |2 fast |0 (OCoLC)fst00871995 | |
| 650 | 7 | |a Metaheuristics |2 fast |0 (OCoLC)fst02000551 | |
| 700 | 1 | |a Abualigah, Laith, |e editor. | |
| 776 | 0 | 8 | |i Print version: |z 0443139253 |z 9780443139253 |w (OCoLC)1389877627 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443139253 |