"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"--
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.
| Preface |
|
ix | |
| Acknowledgements |
|
xi | |
| Authors |
|
xiii | |
|
Chapter 1 Swarm Intelligence: Review, Perspective, and Challenges |
|
|
1 | (10) |
|
|
|
1 | (1) |
|
1.2 History of Swarm Intelligence |
|
|
2 | (3) |
|
1.2.1 Attributes of Metaheuristic Approaches |
|
|
3 | (2) |
|
1.3 Classification and Fundamentals of Swarm Intelligence Algorithms |
|
|
5 | (2) |
|
1.3.1 Fundamentals of Swarm Intelligence Algorithms |
|
|
7 | (1) |
|
|
|
7 | (1) |
|
|
|
8 | (3) |
|
|
|
9 | (2) |
|
Chapter 2 Theory to Practice in PSO |
|
|
11 | (18) |
|
|
|
11 | (1) |
|
|
|
11 | (1) |
|
2.2 Mathematical Modeling |
|
|
11 | (3) |
|
|
|
14 | (5) |
|
2.3.1 Comprehensive Learning Particle Swarm Optimization (CLPSO) |
|
|
14 | (1) |
|
2.3.2 Heterogeneous Comprehensive Learning Particle Swarm Optimization |
|
|
15 | (1) |
|
2.3.3 Extraordinary Particle Swarm Optimization |
|
|
15 | (1) |
|
2.3.4 Improved Random Drift PSO (IRDPSO) |
|
|
15 | (1) |
|
2.3.5 Autonomous Particle Groups for Particle Swarm Optimization (AGPSO) |
|
|
16 | (1) |
|
2.3.6 Improved Particle Swarm Optimization Using Dynamic Parameter Configuration |
|
|
16 | (1) |
|
2.3.6.1 An Enhanced PSO with Time Varying Accelerator Coefficients |
|
|
16 | (1) |
|
2.3.6.2 A Modified PSO with Adaptive Acceleration Coefficients |
|
|
17 | (1) |
|
2.3.6.3 PSO with Asymmetric Time Varying Acceleration Coefficients |
|
|
17 | (1) |
|
2.3.7 Fractional-Order Darwinian PSO |
|
|
17 | (1) |
|
2.3.8 Guaranteed Convergence PSO (GCPSO) |
|
|
18 | (1) |
|
2.3.9 Vector-Evaluated PSO (VEPSO) |
|
|
18 | (1) |
|
|
|
19 | (5) |
|
2.4.1 Hybridization of PSO with Genetic Algorithm |
|
|
19 | (1) |
|
2.4.2 Hybridization of PSO with Differential Evolution (DE) |
|
|
20 | (1) |
|
2.4.3 Hybridization of PSO with Simulated Annealing (SA) |
|
|
21 | (1) |
|
2.4.4 Hybridization of PSO with Cuckoo Search (CS) |
|
|
22 | (1) |
|
2.4.5 Hybridization of PSO using Artificial Bee Colony (ABC) |
|
|
23 | (1) |
|
|
|
24 | (5) |
|
|
|
24 | (5) |
|
Chapter 3 Survey on New Swarm Intelligence Algorithms |
|
|
29 | (30) |
|
|
|
29 | (1) |
|
3.2 Grey Wolf Optimization |
|
|
30 | (3) |
|
3.3 Moth Flame Optimization |
|
|
33 | (5) |
|
3.4 Whale Optimization Algorithm |
|
|
38 | (4) |
|
3.5 Salp Swarm Optimization |
|
|
42 | (3) |
|
3.6 Seagull Optimization Algorithm |
|
|
45 | (4) |
|
3.7 Tunicate Swarm Algorithm |
|
|
49 | (4) |
|
3.8 Comparison of New Swarm Intelligence Algorithms |
|
|
53 | (1) |
|
|
|
54 | (5) |
|
|
|
54 | (5) |
|
Chapter 4 Engineering Applications of Swarm Intelligence |
|
|
59 | (40) |
|
4.1 Application in Electrical Engineering |
|
|
59 | (15) |
|
4.1.1 Problem Formulation |
|
|
61 | (1) |
|
4.1.1.1 Single-Diode Model for PV Cell |
|
|
61 | (1) |
|
4.1.1.2 Double-Diode Model for PV Cell |
|
|
62 | (1) |
|
4.1.1.3 Objective Function |
|
|
63 | (1) |
|
4.1.2 Grey Wolf Optimization |
|
|
63 | (1) |
|
4.1.3 Implementation of GWO for Parameter Extraction |
|
|
64 | (1) |
|
4.1.3.1 Single-Diode Model |
|
|
64 | (1) |
|
4.1.3.2 Double-Diode Model |
|
|
64 | (1) |
|
4.1.4 Experimental Results and Discussion |
|
|
64 | (1) |
|
4.1.4.1 Simulation Results for SDM |
|
|
65 | (1) |
|
4.1.4.2 Simulation Results for DDM |
|
|
66 | (2) |
|
4.1.4.3 Statistical Evaluation with Previously Implemented Algorithms |
|
|
68 | (3) |
|
4.1.5 Findings of GWO-Based Parameter Extraction |
|
|
71 | (3) |
|
4.2 Application in Robotics Engineering |
|
|
74 | (8) |
|
|
|
75 | (1) |
|
|
|
76 | (1) |
|
4.2.3 Framework for Path Planning of Mobile Robot Using FA |
|
|
77 | (1) |
|
4.2.3.1 Problem Definition |
|
|
77 | (1) |
|
4.2.4 Formulation of Fitness Function |
|
|
78 | (2) |
|
4.2.5 Simulation Results and Discussion |
|
|
80 | (2) |
|
4.2.5.1 Convergence Analysis |
|
|
82 | (1) |
|
4.2.6 Findings of Firefly-Based Mobile Robot Path Planning |
|
|
82 | (1) |
|
4.3 Application in Electronics Engineering |
|
|
82 | (9) |
|
|
|
85 | (1) |
|
4.3.2 Conventional DOA Estimation Algorithms |
|
|
86 | (1) |
|
|
|
86 | (1) |
|
|
|
86 | (1) |
|
|
|
87 | (1) |
|
4.3.3 Moth Flame Optimization |
|
|
88 | (1) |
|
4.3.4 Results and Discussion |
|
|
89 | (1) |
|
4.3.4.1 Root-Mean-Square Error |
|
|
89 | (1) |
|
4.3.4.2 Probability of Resolution |
|
|
90 | (1) |
|
|
|
91 | (1) |
|
4.3.5 Findings of MFO-Based Angle of Arrival Estimation |
|
|
91 | (1) |
|
|
|
91 | (8) |
|
|
|
92 | (7) |
|
Chapter 5 Swarm Intelligence Applications in Artificial Neural Networks |
|
|
99 | (24) |
|
|
|
99 | (1) |
|
5.2 Artificial Neural Networks Architecture |
|
|
100 | (2) |
|
5.3 Conventional Learning Algorithm |
|
|
102 | (3) |
|
5.3.1 Back Propagation Algorithm |
|
|
103 | (1) |
|
5.3.2 Levenberg-Marquardt Algorithm |
|
|
104 | (1) |
|
5.4 Swarm Intelligence-Based Artificial Neural Network |
|
|
105 | (13) |
|
5.4.1 Optimization of Weights and Biases of Neural Network |
|
|
106 | (1) |
|
5.4.1.1 Particle Swarm Optimization |
|
|
106 | (3) |
|
5.4.1.2 Ant Colony Optimization |
|
|
109 | (1) |
|
5.4.1.3 Artificial Bee Colony Optimization |
|
|
110 | (1) |
|
5.4.1.4 Ant-Lion Optimization |
|
|
111 | (1) |
|
5.4.1.5 Grey Wolf Optimization |
|
|
112 | (1) |
|
5.4.1.6 Moth Flame Optimization |
|
|
112 | (1) |
|
5.4.1.7 Social Spider Optimization |
|
|
113 | (1) |
|
5.4.2 Optimization of Architecture of Neural Network |
|
|
114 | (1) |
|
5.4.2.1 Particle Swarm Optimization |
|
|
114 | (1) |
|
5.4.2.2 Ant-Lion Optimization |
|
|
115 | (1) |
|
5.4.3 Hybridization of Swarm Intelligence Algorithm with Gradient-Based Algorithm |
|
|
116 | (2) |
|
|
|
118 | (5) |
|
|
|
119 | (4) |
| Index |
|
123 | |
Abhishek Sharma received the bachelors degree in Electronics and Communication Engineering from ITM-Gwalior, India, in 2012, and the masters degree in Robotics Engineering from the University of Petroleum and Energy Studies (UPES), Dehradun, India, in 2014. He was a Senior Research Fellow in a DST funded project under the Technology Systems Development Scheme. He was an Assistant Professor with the Department of Electronics and Instrumentation, UPES. He is working as a research scientist in research and development department, UPES. His research interests include embedded system, optimization, swarm intelligence and robotics
Abhinav Sharma received his B. Tech from H. N. B. Garhwal University, Srinagar, India, in 2009 and the M. Tech from Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, India, in 2011. He did his Ph. D. from Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, India, in 2016. He is working as an Assistant Professor (Senior Scale) in the Department of Electrical and Electronics Engineering in University of Petroleum and Energy Studies, Dehradun. His field of interest includes adaptive array signal processing, Artificial Intelligence, and Machine Learning, Optimization Techniques and smart antenna
Jitendra Kumar Pandey was born in Bakeware, Uttar Pradesh, India. He received his BSc (Biochemistry, 1996) and MSc (Organic Chemistry, 1998), from the University of Kanpur, India. He has received is Ph.D. degree from National Chemical Laboratory Pune, in the area of polymer science and his thesis title was "Degradability of Polymer Composites from Renewable Resources". He worked as business development manager-International Business SRL Ranbaxy Ltd., Andheri-East, India. He is working as a Professor and Dean School of Basic & Applied Science, Adamas University, West Bengal, India. His area of expertise is Polymer nanocomposites, Bio-composites, Natural Nano-particles, Water treatment.
Mangey Ram received the Ph.D. degree major in Mathematics and minor in Computer Science from G. B. Pant University of Agriculture and Technology, Pantnagar, India. He has been a Faculty Member for around twelve years and has taught several core courses in pure and applied mathematics at undergraduate, postgraduate, and doctorate levels. He is currently a Research Professor at Graphic Era (Deemed to be University), Dehradun, India. Before joining the Graphic Era, he was a Deputy Manager (Probationary Officer) with Syndicate Bank for a short period. He is Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, Book Series Editor with Elsevier, CRC Press-A Taylor, and Frances Group, De Gruyter Publisher Germany, River Publisher, USA and the Guest Editor & Member of the editorial board of various journals. He has published 225 plus research publications in IEEE, Taylor & Francis, Springer, Elsevier, Emerald, World Scientific, and many other national and international journals and conferences. His fields of research are reliability theory and applied mathematics. Dr. Ram is a Senior Member of the IEEE, life member of Operational Research Society of India, Society for Reliability Engineering, Quality and Operations Management in India, Indian Society of Industrial and Applied Mathematics, He has been a member of the organizing committee of several international and national conferences, seminars, and workshops. He has been conferred with the "Young Scientist Award" by the Uttarakhand State Council for Science and Technology, Dehradun, in 2009. He has been awarded the "Best Faculty Award" in 2011; "Research Excellence Award" in 2015; and recently "Outstanding Researcher Award" in 2018 for his significant contribution to academics and research at Graphic Era Deemed to be University, Dehradun, India.
