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

✍ Scribed by Abhishek Sharma, Abhinav Sharma, Jitendra Kumar Pandey, Mangey Ram

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 141
• Edition: 1
• Category: Library

✦ Synopsis

Swarm intelligence is one of the fastest-growing sub-fields of artificial intelligence and soft computing. This field includes multiple optimization algorithms to solve NP-hard problems for which conventional methods are not effective. It inspires researchers in engineering sciences to learn theories from nature and incorporate them.

Swarm Intelligence: Foundation, Principles, and Engineering Applications provides a comprehensive review of new swarm intelligence techniques and offers practical implementation of Particle Swarm Optimization (PSO) with MATLAB code. The book discusses the statistical analysis of swarm optimization techniques so that researchers can analyze their experiment design. It also includes algorithms in social sectors, oil and gas industries, and recent research findings of new optimization algorithms in the field of engineering describing the implementation in Machine Learning.

This book is written for students of engineering, research scientists, and academicians involved in the engineering sciences.

✦ Table of Contents

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