Comprehensive Metaheuristics: Algorithms and Applications

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Comprehensive Metaheuristics: Algorithms and Applications
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Contents
Contributors
Chapter 1: Chaos theory in metaheuristics
	1. Introduction
	2. Chaos system and chaotic maps
		2.1. Chebyshev map
		2.2. Circle map
		2.3. Gaussian and gauss-mouse map
		2.4. Iterative map
		2.5. Logistic map
		2.6. Piecewise map
		2.7. Sine map
		2.8. Singer map
		2.9. Sinusoidal map
		2.10. Tent map
	3. Chaotic strategies in metaheuristic optimization
		3.1. Studies using different chaotic strategies in metaheuristics
	4. An application with chaotic system
	References
Chapter 2: Metaheuristic approaches for solving multiobjective optimization problems
	1. Introduction
		1.1. Definitions
	2. Related works
	3. An overview of electric fish optimization
		3.1. Initialization of population
		3.2. Active and passive electrolocation phases
	4. Multiobjective electric fish optimization algorithm
		4.1. Population initialization
		4.2. Frequency update
		4.3. Active and passive electrolocation phases
	5. Experiments
		5.1. Benchmark problems
		5.2. Performance metrics
			5.2.1. Hypervolume (HV)
			5.2.2. SPREAD
			5.2.3. EPSILON
			5.2.4. Inverted generational distance (IGD)
		5.3. Competitor algorithms
		5.4. Performance evaluation on MOOP benchmark sets
			5.4.1. Parameter tuning
			5.4.2. Results
		5.5. Performance evaluation on MaOP benchmark set
			5.5.1. Parameter tuning
			5.5.2. Results
	6. Conclusion
	References
Chapter 3: A brief overview of physics-inspired metaheuristics
	1. Introduction
	2. Classical mechanics-based metaheuristics
		2.1. Central force optimization
		2.2. Gravitational search algorithm
		2.3. Colliding bodies optimization
		2.4. Equilibrium optimizer
	3. Fluid mechanics-based metaheuristics
		3.1. Vortex search algorithm
		3.2. Flow regime algorithm
		3.3. Archimedes optimization algorithm
	4. Thermodynamics-based metaheuristics
		4.1. Thermal exchange optimization
		4.2. States of matter search
		4.3. Kinetic gas molecules optimization
		4.4. Henry gas solubility optimizer
	5. Electromagnetism-based metaheuristics
		5.1. Artificial electric field algorithm
		5.2. Magnetic-inspired optimization
		5.3. Electromagnetic field algorithm
		5.4. Ions motion optimization
	6. Optics-based metaheuristics
		6.1. Ray optimization
		6.2. Optics-inspired optimization
	7. Other physics-based metaheuristic algorithms
		7.1. Simulated annealing
		7.2. Multiverse optimizer
		7.3. Atom search optimization
		7.4. Nuclear reaction optimization
	8. Conclusion
	References
Chapter 4: Evolutionary computation techniques for optimal response actions against water distribution networks contamination
	1. Introduction
	2. Evolutionary computation
		2.1. Development and evaluation of the solution candidate
			2.1.1. Representation schemes
			2.1.2. Initial population
			2.1.3. Fitness function
		2.2. Applying the reproduction operators
		2.3. Applying the selection operator
		2.4. Identifying the termination criterion
		2.5. Genetic algorithm
			2.5.1. Multiobjective optimization
				Weighted aggregation
				Nondominated sorting genetic algorithm
	3. Methodology development and applications
		3.1. Development of response actions
			3.1.1. Response action objectives
			3.1.2. Defined decision variables
			3.1.3. Optimal response actions
		3.2. Optimization algorithm modifications
			3.2.1. Noisy genetic algorithm
			3.2.2. Modified NSGA-II
	4. Conclusion
	References
Chapter 5: Metaheuristic technique for solving fuzzy nonlinear equations
	1. Introduction
	2. Preliminaries
	3. A brief description of the GBO algorithm
		3.1. Initialization phase
		3.2. Gradient-based optimizer phases
			3.2.1. Gradient search rule (GSR) phase
			3.2.2. Local escaping operator (LEO) phase [14]
	4. Numerical examples
	5. Conclusion
	Acknowledgments
	References
Chapter 6: Metaheuristic algorithms in network intrusion detection
	1. Introduction
	2. Metaheuristic algorithms
	3. Methodology
	4. Metaheuristic algorithms in IDS
		4.1. Genetic algorithm (GA)
		4.2. Particle swarm optimization (PSO)
		4.3. Gray wolf optimization algorithm (GWO)
		4.4. Grasshopper optimization algorithm (GOA)
		4.5. Artificial bee colony (ABC)
		4.6. Ant colony optimization (ACO)
		4.7. Differential evolution (DE)
		4.8. Firefly optimization algorithm (FFA)
		4.9. Other metaheuristic algorithms
		4.10. Hybrid metaheuristics algorithms
	5. Challenges and future direction
	6. Conclusion
	References
Chapter 7: Metaheuristic algorithms in text clustering
	1. Introduction
	2. Text clustering formulation procedure
		2.1. Design and problem descriptions
		2.2. Preprocessing
			2.2.1. Tokenization
			2.2.2. Stop word removal
			2.2.3. Stemming
		2.3. Document representation
		2.4. Solution representation
		2.5. Objective function
	3. Metaheuristic algorithms application in text clustering
		3.1. Multiverse optimizer (MVO)
		3.2. Particle swarm optimization (PSO)
			3.2.1. Gray wolf optimizer (GWO)
			3.2.2. Cuckoo search (CS)
			3.2.3. Genetic algorithm (GA)
			3.2.4. Firefly algorithm (FA)
			3.2.5. Krill herd algorithm (KHA)
			3.2.6. Social spider optimization (SSO)
			3.2.7. Whale optimization algorithm (WOA)
			3.2.8. Ant colony optimization (ACO)
			3.2.9. Harmony search (HS)
			3.2.10. Differential evolution (DE)
		3.3. Other metaheuristic algorithms
	4. Conclusion and possible future research focus
	References
Chapter 8: Application of metaheuristic algorithms in optimal design of sewer collection systems
	1. Introduction
	2. Sewer collection systems
		2.1. Basics
		2.2. Layout design
		2.3. Hydraulic design
		2.4. Objective function
		2.5. Constraints
	3. Metaheuristic algorithms
		3.1. Ant colony optimization (ACO)
		3.2. Genetic algorithm (GA)
		3.3. Particle swarm optimization (PSO)
		3.4. Simulated annealing (SA)
		3.5. Tabu search (TS)
	4. Applications
	5. Summary and conclusion
	References
Chapter 9: Space truss structures optimization using metaheuristic optimization algorithms
	1. Introduction
	2. African vulture optimization algorithm, artificial gorilla troops optimizer, and artificial hummingbird algorithms
		2.1. African Vulture's Optimization Algorithm (AVOA)
		2.2. Artificial gorilla troops optimizer (AGTO)
		2.3. Artificial hummingbird algorithm (AHA)
	3. Structural design optimization
		3.1. The 25-bar space truss design problem
		3.2. The 72-bar space truss design problem
	4. Conclusion
	References
Chapter 10: Metaheuristics for solving the wind turbine placement problem
	1. Introduction
	2. Artificial gorilla troops optimizers
	3. Binary variants of artificial gorilla troops optimizers
		3.1. Transfer functions
		3.2. Binary variants of artificial gorilla troops optimizers
	4. Experimental setup
		4.1. Wind turbine placement problem (WTPP)
	5. Results and discussion
	6. Conclusion
	References
Chapter 11: Use of metaheuristics in industrial development and their future perspectives
	1. Introduction
	2. Classification of metaheuristics
		2.1. Evolutionary algorithm
		2.2. Trajectory based
		2.3. Nature inspired
		2.4. Ancient inspired
	3. Optimization in industry
		3.1. Metaheuristics in industrial optimization
	4. Future perspective of metaheuristics in industrial development
		4.1. Industry 4.0
	5. Conclusion
	References
Chapter 12: Lévy flight and Chaos theory based metaheuristics for grayscale image thresholding
	1. Introduction
	2. Literature survey
	3. Gravitational search algorithm
	4. Lévy flight and chaos theory-based gravitational search algorithm
		4.1. Lévy flight
		4.2. Chaos theory
		4.3. Mathematical model of the LCGSA algorithm
	5. Image segmentation using LCGSA technique
	6. Experimental results and discussion
		6.1. Simulation results of cameraman image
		6.2. Simulation results of Lena image
	7. Conclusion and future scope
	Conflict of interest
	References
Chapter 13: Metaheuristics for optimal feature selection in high-dimensional datasets
	1. Introduction
	2. Characteristics of high-dimensional data
		2.1. High dimensionality
		2.2. Limited sample size
		2.3. Class imbalance
		2.4. Label noise
		2.5. Inherent attributes of microarray data
	3. Feature selection in high-dimensional datasets
	4. Metaheuristic approaches
		4.1. Major metaheuristics as feature selectors in high-dimensional data
			4.1.1. Particle swarm optimization
			4.1.2. Artificial bee colony
			4.1.3. Invasive weed optimization
			4.1.4. Bat algorithm
			4.1.5. Gray wolf optimization
		4.2. Improved metaheuristics as feature selectors in high-dimensional data
		4.3. Hybrid metaheuristics as feature selectors in in high-dimensional data
	5. Practical evaluation
		5.1. Analysis of major metaheuristics as feature selectors in high-dimensional datasets
			5.1.1. Datasets
			5.1.2. Data preprocessing
			5.1.3. Results
		5.2. Analysis of improved metaheuristics as feature selectors in high-dimensional datasets
			5.2.1. Datasets
			5.2.2. Results
		5.3. Analysis of hybrid metaheuristics as feature selectors in high-dimensional datasets
			5.3.1. Considered biomedical datasets
			5.3.2. Results
	6. Conclusion
	References
Chapter 14: Optimal deployment of sensors for leakage detection in water distribution systems using metaheuristics
	1. Introduction
	2. Background
	3. Related works
	4. Methods
		4.1. Leakage simulation
		4.2. Candidate locations for sensor deployment
		4.3. Candidate pipes for flow sensor deployment
		4.4. Candidate nodes for pressure sensor deployment
	5. Objective functions
		5.1. Minimizing the number of sensors
		5.2. Minimizing the leak detection time
		5.3. Maximizing the value of information (VOI)
		5.4. Minimizing the transinformation entropy (TE)
		5.5. Minimizing the extra information derived from pressure sensors
		5.6. Maximizing the sensor coverage
		5.7. Maximizing the accuracy of identified leakage zone
	6. Computational experiments
	7. Conclusions
	References
Chapter 15: Metaheuristic-based automatic generation controller in interconnected power systems with renewable energy sources
	1. Introduction
	2. Renewable energy sources integrated power systems
	3. The proposed PID-(1+I) controller
	4. Metaheuristic optimization techniques
		4.1. Gorilla troops optimizer (GTO)
		4.2. African vulture optimization algorithm (AVOA)
		4.3. Honey badger algorithm (HBA)
	5. Simulation results and discussions
		5.1. Case study I: Random load change in Area-1
		5.2. Case study II: Different step load change in area-2
		5.3. Case study III: The effect of RES generation
		5.4. Case study IV: Sensitivity analysis of system parameters
	6. Conclusion
	References
Chapter 16: Route optimization in MANET using swarm intelligence algorithm
	1. Introduction
	2. Related work
	3. Workflow of MANETs
		3.1. Limitations of MANETs
			3.1.1. Hop count
			3.1.2. Node mobility
			3.1.3. Delay
	4. Routing challenges in MANETs
	5. Routing issues resolved by optimization
		5.1. Genetic algorithm (GA)
		5.2. Particle swam optimization (PSO)
		5.3. Ant Colony Optimization (ACO)
		5.4. Artificial Bee Colony (ABC)
		5.5. Bacterial foraging optimization algorithm (BFOA)
		5.6. Bat optimization
		5.7. Gray Wolf Optimization
		5.8. Dolphin optimization
	6. Comparative analysis
	7. Conclusion
	References
Chapter 17: The promise of metaheuristic algorithms for efficient operation of a highly complex power system
	1. Introduction
	2. Reptile search algorithm
	3. Problem statement
		3.1. Nonlinear model of multimachine power system
		3.2. Power system stabilizer structure
		3.3. Linearized system model
	4. Case study
		4.1. Test system
		4.2. Developed Simulink model
		4.3. System analysis without PSS and optimal PSS location
		4.4. Objective function and PSS tuning via RSA
		4.5. Statistical performance of RSA
		4.6. System stability in linear model
		4.7. System stability in nonlinear model
	5. Conclusions
	References
Chapter 18: Genome sequence assembly using metaheuristics
	1. Introduction
	2. Past works
	3. Genome sequencing
	4. Combinatorial optimization
		4.1. Fitness function
			4.1.1. Overlap-based fitness
			4.1.2. Smith-Waterman algorithm
		4.2. Shortest position value (SPV) rule
	5. Experiments
		5.1. Datasets
		5.2. Parameters
		5.3. Results
		5.4. Convergence analysis
		5.5. Statistical significance
	6. Conclusions
	References
Chapter 19: Metaheuristics for optimizing weights in neural networks
	1. Introduction
	2. Feedforward neural networks
	3. Proposed algorithm
		3.1. Inspiration of HOA
		3.2. Algorithmic steps of HOA
		3.3. Proposed algorithm
	4. Experiments and results
		4.1. Test datasets
		4.2. Experimental settings
		4.3. Comparison with other swarm-based optimization algorithms
			4.3.1. Convergence analysis
			4.3.2. Friedmans statistical test
	5. Conclusion and future work
	References
Chapter 20: Metaheuristics for clustering problems
	1. Introduction
	2. Data clustering problem
	3. Data clustering using metaheuristic algorithms
	4. Results and discussion
		4.1. Results of conventional metaheuristics
		4.2. Results of recent metaheuristics
	5. Conclusion and future works
	References
Chapter 21: Employment of bio-inspired algorithms in the field of antenna array optimization: A review
	1. Introduction
	2. Flower pollination algorithm
	3. Cat Swarm Optimization
		3.1. Searching mode
		3.2. Tracking mode
	4. Gravitational Search Algorithm
	5. Case study
	6. Conclusion
	References
Chapter 22: Foundations of combinatorial optimization, heuristics, and metaheuristics
	1. Introduction
	2. Combinatorial optimization problems
	3. Analysis of algorithms
	4. Complexity of algorithms
	5. Modeling a CO problem
		5.1. Graph theory concepts
		5.2. Mathematical optimization model
			5.2.1. Linear programming
			5.2.2. Integer linear programming
			5.2.3. Mixed-integer linear programming
			5.2.4. LP solvers
		5.3. Constraint programming
			5.3.1. Constraint propagation
			5.3.2. Global constraints
			5.3.3. Systematic search
	6. Solution methods
		6.1. Exact algorithms
			6.1.1. Branching algorithms
			6.1.2. Dynamic programming
		6.2. Heuristics
			6.2.1. The constructive heuristics
			6.2.2. The improvement heuristics
		6.3. Metaheuristics
			6.3.1. Simulated annealing
			6.3.2. The tabu search
			6.3.3. Greedy randomized adaptive search procedure (GRASP)
			6.3.4. Ant colony optimization (ACO)
			6.3.5. Genetic algorithms
	7. Conclusion
	References
Index
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