Swarm Intelligence: Foundation, Principles, and Engineering Applications (Mathematical Engineering, Manufacturing, and Management Sciences)

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgements
Authors
Chapter 1 Swarm Intelligence: Review, Perspective, and Challenges
	1.1 Introduction
	1.2 History of Swarm Intelligence
		1.2.1 Attributes of Metaheuristic Approaches
	1.3 Classification and Fundamentals of Swarm Intelligence Algorithms
		1.3.1 Fundamentals of Swarm Intelligence Algorithms
	1.4 Theories of Swarm
	1.5 Conclusion
	References
Chapter 2 Theory to Practice in PSO
	2.1 Introduction
		2.1.1 Overview of PSO
	2.2 Mathematical Modeling
	2.3 Advances in PSO
		2.3.1 Comprehensive Learning Particle Swarm Optimization (CLPSO)
		2.3.2 Heterogeneous Comprehensive Learning Particle Swarm Optimization
		2.3.3 Extraordinary Particle Swarm Optimization
		2.3.4 Improved Random Drift PSO (IRDPSO)
		2.3.5 Autonomous Particle Groups for Particle Swarm Optimization (AGPSO)
		2.3.6 Improved Particle Swarm Optimization Using Dynamic Parameter Configuration
			2.3.6.1 An Enhanced PSO with Time Varying Accelerator Coefficients
			2.3.6.2 A Modified PSO with Adaptive Acceleration Coefficients
			2.3.6.3 PSO with Asymmetric Time Varying Acceleration Coefficients
		2.3.7 Fractional-Order Darwinian PSO
		2.3.8 Guaranteed Convergence PSO (GCPSO)
		2.3.9 Vector-Evaluated PSO (VEPSO)
	2.4 Hybrid PSO
		2.4.1 Hybridization of PSO with Genetic Algorithm
		2.4.2 Hybridization of PSO with Differential Evolution (DE)
		2.4.3 Hybridization of PSO with Simulated Annealing (SA)
		2.4.4 Hybridization of PSO with Cuckoo Search (CS)
		2.4.5 Hybridization of PSO using Artificial Bee Colony (ABC)
	2.5 Conclusion
	References
Chapter 3 Survey on New Swarm Intelligence Algorithms
	3.1 Introduction
	3.2 Grey Wolf Optimization
	3.3 Moth Flame Optimization
	3.4 Whale Optimization Algorithm
	3.5 Salp Swarm Optimization
	3.6 Seagull Optimization Algorithm
	3.7 Tunicate Swarm Algorithm
	3.8 Comparison of New Swarm Intelligence Algorithms
	3.9 Conclusion
	References
Chapter 4 Engineering Applications of Swarm Intelligence
	4.1 Application in Electrical Engineering
		4.1.1 Problem Formulation
			4.1.1.1 Single-Diode Model for PV Cell
			4.1.1.2 Double-Diode Model for PV Cell
			4.1.1.3 Objective Function
		4.1.2 Grey Wolf Optimization
		4.1.3 Implementation of GWO for Parameter Extraction
			4.1.3.1 Single-Diode Model
			4.1.3.2 Double-Diode Model
		4.1.4 Experimental Results and Discussion
			4.1.4.1 Simulation Results for SDM
			4.1.4.2 Simulation Results for DDM
			4.1.4.3 Statistical Evaluation with Previously Implemented Algorithms
		4.1.5 Findings of GWO-Based Parameter Extraction
	4.2 Application in Robotics Engineering
		4.2.1 Related Work
		4.2.2 Firefly Algorithm
		4.2.3 Framework for Path Planning of Mobile Robot Using FA
			4.2.3.1 Problem Definition
		4.2.4 Formulation of Fitness Function
		4.2.5 Simulation Results and Discussion
			4.2.5.1 Convergence Analysis
		4.2.6 Findings of Firefly-Based Mobile Robot Path Planning
	4.3 Application in Electronics Engineering
		4.3.1 Data Model
		4.3.2 Conventional DOA Estimation Algorithms
			4.3.2.1 CAPON
			4.3.2.2 MUSIC
			4.3.2.3 ESPRIT
		4.3.3 Moth Flame Optimization
		4.3.4 Results and Discussion
			4.3.4.1 Root-Mean-Square Error
			4.3.4.2 Probability of Resolution
			4.3.4.3 Convergence Plot
		4.3.5 Findings of MFO-Based Angle of Arrival Estimation
		4.4 Conclusion
		References
Chapter 5 Swarm Intelligence Applications in Artificial Neural Networks
	5.1 Introduction
	5.2 Artificial Neural Networks Architecture
	5.3 Conventional Learning Algorithm
		5.3.1 Back Propagation Algorithm
		5.3.2 Levenberg-Marquardt Algorithm
	5.4 Swarm Intelligence-Based Artificial Neural Network
		5.4.1 Optimization of Weights and Biases of Neural Network
			5.4.1.1 Particle Swarm Optimization
			5.4.1.2 Ant Colony Optimization
			5.4.1.3 Artificial Bee Colony Optimization
			5.4.1.4 Ant-Lion Optimization
			5.4.1.5 Grey Wolf Optimization
			5.4.1.6 Moth Flame Optimization
			5.4.1.7 Social Spider Optimization
		5.4.2 Optimization of Architecture of Neural Network
			5.4.2.1 Particle Swarm Optimization
			5.4.2.2 Ant-Lion Optimization
		5.4.3 Hybridization of Swarm Intelligence Algorithm with Gradient-Based Algorithm
	5.5 Conclusion
	References
Index