book

Swarm Intelligence

Name: Swarm Intelligence
ISBN: 9781000529753

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

February 2022

Intermediate to advanced

140 pages

4h 24m

English

CRC Press

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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 Introduction1.2 History of Swarm Intelligence1.2.1 Attributes of Metaheuristic Approaches1.3 Classification and Fundamentals of Swarm Intelligence Algorithms1.3.1 Fundamentals of Swarm Intelligence Algorithms1.4 Theories of Swarm1.5 ConclusionReferences

Chapter 2 Theory to Practice in PSO
2.1 Introduction 2.1.1 Overview of PSO2.2 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 Configuration2.3.6.1 An Enhanced PSO with Time Varying Accelerator Coefficients2.3.6.2 A Modified PSO with Adaptive Acceleration Coefficients2.3.6.3 PSO with Asymmetric Time Varying Acceleration Coefficients2.3.7 Fractional-Order Darwinian PSO2.3.8 Guaranteed Convergence PSO (GCPSO)2.3.9 Vector-Evaluated PSO (VEPSO)2.4 Hybrid PSO2.4.1 Hybridization of PSO with Genetic Algorithm2.4.2 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 Introduction3.2 Grey Wolf Optimization3.3 Moth Flame Optimization 3.4 Whale Optimization Algorithm3.5 Salp Swarm Optimization3.6 Seagull Optimization Algorithm3.7 Tunicate Swarm Algorithm3.8 Comparison of New Swarm Intelligence Algorithms3.9 Conclusion References
Chapter 4 Engineering Applications of Swarm Intelligence
4.1 Application in Electrical Engineering4.1.1 Problem Formulation4.1.1.1 Single-Diode Model for PV Cell4.1.1.2 Double-Diode Model for PV Cell4.1.1.3 Objective Function4.1.2 Grey Wolf Optimization4.1.3 Implementation of GWO for Parameter Extraction 4.1.3.1 Single-Diode Model4.1.3.2 Double-Diode Model4.1.4 Experimental Results and Discussion4.1.4.1 Simulation Results for SDM4.1.4.2 Simulation Results for DDM4.1.4.3 Statistical Evaluation with Previously Implemented Algorithms4.1.5 Findings of GWO-Based Parameter Extraction4.2 Application in Robotics Engineering 4.2.1 Related Work4.2.2 Firefly Algorithm4.2.3 Framework for Path Planning of Mobile Robot Using FA4.2.3.1 Problem Definition4.2.4 Formulation of Fitness Function4.2.5 Simulation Results and Discussion4.2.5.1 Convergence Analysis4.2.6 Findings of Firefly-Based Mobile Robot Path Planning4.3 Application in Electronics Engineering4.3.1 Data Model4.3.2 Conventional DOA Estimation Algorithms 4.3.2.1 CAPON4.3.2.2 MUSIC 4.3.2.3 ESPRIT 4.3.3 Moth Flame Optimization4.3.4 Results and Discussion4.3.4.1 Root-Mean-Square Error4.3.4.2 Probability of Resolution 4.3.4.3 Convergence Plot4.3.5 Findings of MFO-Based Angle of Arrival Estimation 4.4 ConclusionReferences
Chapter 5 Swarm Intelligence Applications in Artificial Neural Networks
5.1 Introduction5.2 Artificial Neural Networks Architecture5.3 Conventional Learning Algorithm 5.3.1 Back Propagation Algorithm5.3.2 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 Optimization5.4.1.2 Ant Colony Optimization5.4.1.3 Artificial Bee Colony Optimization5.4.1.4 Ant-Lion Optimization5.4.1.5 Grey Wolf Optimization5.4.1.6 Moth Flame Optimization5.4.1.7 Social Spider Optimization5.4.2 Optimization of Architecture of Neural Network5.4.2.1 Particle Swarm Optimization5.4.2.2 Ant-Lion Optimization5.4.3 Hybridization of Swarm Intelligence Algorithm with Gradient-Based Algorithm 5.5 Conclusion References
Index

Overview

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.

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