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 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 algorithmsHelps readers formulate and design the best optimization algorithms for their research goals through case studies in a variety of real-world applicationsHelps readers understand the links between Metaheuristic algorithms and their application in Computational Intelligence, Machine Learning, and Deep Learning problems 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