Table Of ContentSwarm Intelligence
Scrivener Publishing
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Concise Introductions to AI and Data Science
Series Editor: Dr. Prasenjit Chatterjee, MCKV Institute of Engineering, West Bengal, India;
Dr. Loveleen Gaur, Amity International Business School (AIBS), India; and Dr. Morteza
Yazdani, ESIC Business & Marketing School, Madrid, Spain
Scope: Reflecting the interdisciplinary and thematic nature of the series, Concise Introductions to (AI)
and Data Science presents cutting-edge research and practical applications in the area of Artificial
Intelligence and data science. The series aims to share new approaches and innovativ e perspectives in
AI and data analysis from diverse engineering domains to find pragmatic and futuristic solutions for
society at large. It publishes peer-reviewed and authoritative scholarly works on theoretical foundations,
algorithms, models, applications and case studies on specific issues. The monographs and edited volumes
will be no more than 75,000 words.
Please send proposals to one of the 3 editors:
[email protected]; [email protected]; [email protected]
Publishers at Scrivener
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Phillip Carmical ([email protected])
Scrivener Publishing
100 Cummings Center, Suite 541J
Beverly, MA 01915-6106
Concise Introductions to AI and Data Science
Swarm Intelligence
Series Editor: Dr. Prasenjit Chatterjee, MCKV Institute of Engineering, West Bengal, India;
Dr. Loveleen Gaur, Amity International Business School (AIBS), India; and Dr. Morteza
Yazdani, ESIC Business & Marketing School, Madrid, Spain
Scope: Reflecting the interdisciplinary and thematic nature of the series, Concise Introductions to (AI)
An Approach from Natural
and Data Science presents cutting-edge research and practical applications in the area of Artificial
Intelligence and data science. The series aims to share new approaches and innovativ e perspectives in
AI and data analysis from diverse engineering domains to find pragmatic and futuristic solutions for to Artificial
society at large. It publishes peer-reviewed and authoritative scholarly works on theoretical foundations,
algorithms, models, applications and case studies on specific issues. The monographs and edited volumes
will be no more than 75,000 words.
Please send proposals to one of the 3 editors:
[email protected]; [email protected]; [email protected]
Publishers at Scrivener
Martin Scrivener ([email protected])
Kuldeep Singh Kaswan
Phillip Carmical ([email protected])
School of Computing Science & Engineering, Galgotias University, Greater Noida,
Uttar Pradesh, India
Jagjit Singh Dhatterwal
Department of Artificial Intelligence & Data Science, Koneru Lakshmaiah
Education Foundation, Vaddeswaram, AP, India
and
Avadhesh Kumar
Pro Vice-Chancellor, Galgotias University, Greater Noida, Uttar Pradesh, India
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-86506-3
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Contents
Preface xi
1 Introduction of Swarm Intelligence 1
1.1 Introduction to Swarm Behavior 1
1.1.1 Individual vs. Collective Behaviors 1
1.2 Concepts of Swarm Intelligence 2
1.3 Particle Swarm Optimization (PSO) 2
1.3.1 Main Concept of PSO 3
1.4 Meaning of Swarm Intelligence 3
1.5 What Is Swarm Intelligence? 4
1.5.1 Types of Communication Between Swarm Agents 4
1.5.2 Examples of Swarm Intelligence 4
1.6 History of Swarm Intelligence 5
1.7 Taxonomy of Swarm Intelligence 6
1.8 Properties of Swarm Intelligence 10
1.8.1 Models of Swarm Behavior 11
1.8.2 Self-Propelled Particles 11
1.9 Design Patterns in Cyborg Swarm 12
1.9.1 Design Pattern Creation 14
1.9.2 Design Pattern Primitives and Their Representation 16
1.10 Design Patterns Updating in Cyborg 19
1.10.1 Behaviors and Data Structures 20
1.10.2 Basics of Cyborg Swarming 20
1.10.3 Information Exchange at Worksites 21
1.10.4 Information Exchange Center 22
1.10.5 Working Features of Cyborg 23
1.10.6 Highest Utility of Cyborg 24
1.10.7 Gain Extra Reward 25
1.11 Property of Design Cyborg 25
1.12 Extending the Design of Cyborg 31
1.12.1 Information Storage in Cyborg 32
v
vi Contents
1.12.2 Information Exchange Any Time 34
1.12.3 The New Design Pattern Rules in Cyborg 34
1.13 Bee-Inspired Cyborg 35
1.14 Conclusion 36
2 Foundation of Swarm Intelligence 37
2.1 Introduction 37
2.2 Concepts of Life and Intelligence 38
2.2.1 Intelligence: Good Minds in People and Machines 40
2.2.2 Intelligence in People: The Boring Criterion 41
2.2.3 Intelligence in Machines: The Turing Criterion 42
2.3 Symbols, Connections, and Optimization by Trial and Error 43
2.3.1 Problem Solving and Optimization 43
2.3.2 A Super-Simple Optimization Problem 44
2.3.3 Three Spaces of Optimization 45
2.3.4 High-Dimensional Cognitive Space and Word
Meanings 46
2.4 The Social Organism 49
2.4.1 Flocks, Herds, Schools and Swarms: Social Behavior
as Optimization 50
2.4.2 Accomplishments of the Social Insects 51
2.4.3 Optimizing with Simulated Ants: Computational
Swarm Intelligence 52
2.5 Evolutionary Computation Theory and Paradigms 54
2.5.1 The Four Areas of Evolutionary Computation 54
2.5.2 Evolutionary Computation Overview 57
2.5.3 Evolutionary Computing Technologies 57
2.6 Humans – Actual, Imagined, and Implied 58
2.6.1 The Fall of the Behaviorist Empire 59
2.7 Thinking is Social 61
2.7.1 Adaptation on Three Levels 62
2.8 Conclusion 62
3 The Particle Swarm and Collective Intelligence 65
3.1 The Particle Swarm and Collective Intelligence 65
3.1.1 Socio-Cognitive Underpinnings: Evaluate, Compare,
and Imitate 66
3.1.2 A Model of Binary Decision 68
3.1.3 The Particle Swarm in Continuous Numbers 70
3.1.4 Pseudocode for Particle Swarm Optimization
in Continuous Numbers 71
Contents vii
3.2 Variations and Comparisons 72
3.2.1 Variations of the Particle Swarm Paradigm 72
3.2.2 Parameter Selection 72
3.2.3 Vmax 72
3.2.4 Controlling the Explosion 73
3.2.5 Simplest Constriction 73
3.2.6 Neighborhood Topology 74
3.2.7 Sociometric of the Particle Swarm 74
3.2.8 Selection and Self-Organization 76
3.2.9 Ergodicity: Where Can It Go from Here? 77
3.2.10 Convergence of Evolutionary Computation
and Particle Swarms 78
3.3 Implications and Speculations 78
3.3.1 Assertions in Cuckoo Search 79
3.3.2 Particle Swarms Are a Valuable Soft Intelligence
(Machine Learning Intelligent) Approach 80
3.3.3 Information and Motivation 82
3.3.4 Vicarious vs. Direct Experience 83
3.3.5 The Spread of Influence 83
3.3.6 Machine Adaptation 84
3.3.7 Learning or Adaptation? 85
3.4 Conclusion 86
4 Algorithm of Swarm Intelligence 89
4.1 Introduction 89
4.1.1 Methods for Alternate Stages of Model
Parameter Reform 90
4.1.2 Ant Behavior 90
4.2 Ant Colony Algorithm 92
4.3 Artificial Bee Colony Optimization 95
4.3.1 The Artificial Bee Colony 96
4.4 Cat Swarm Optimization 98
4.4.1 Original CSO Algorithm 98
4.4.2 Description of the Global Version of CSO Algorithm 100
4.4.3 Seeking Mode (Resting) 100
4.4.4 Tracing Mode (Movement) 101
4.4.5 Description of the Local Version of CSO Algorithm 101
4.5 Crow Search Optimization 103
4.5.1 Original CSA 104
4.6 Elephant Intelligent Behavior 105
4.6.1 Elephant Herding Optimization 107
viii Contents
4.6.2 Position Update of Elephants in a Clan 108
4.6.3 Pseudocode of EHO Flowchart 109
4.7 Grasshopper Optimization 109
4.7.1 Description of the Grasshopper Optimization
Algorithm 111
4.8 Conclusion 112
5 Novel Swarm Intelligence Optimization Algorithm (SIOA) 113
5.1 Water Wave Optimization 113
5.1.1 Objective Function 115
5.1.2 Power Balance Constraints 115
5.1.3 Generator Capacity Constraints 116
5.1.4 Water Wave Optimization Algorithm 116
5.1.5 Mathematical Model of WWO Algorithm 117
5.1.6 Implementation of WWO Algorithm
for ELD Problem 118
5.2 Brain Storm Optimization 119
5.2.1 Multi-Objective Brain Storm Optimization Algorithm 120
5.2.2 Clustering Strategy 120
5.2.3 Generation Process 121
5.2.4 Mutation Operator 122
5.2.5 Selection Operator 122
5.2.6 Global Archive 123
5.3 Whale Optimization Algorithm 123
5.3.1 Description of the WOA 124
5.4 Conclusion 125
6 Swarm Cyborg 127
6.1 Introduction 127
6.1.1 Swarm Intelligence Cyborg 129
6.2 Swarm Cyborg Taxis Algorithms 132
6.2.1 Cyborg Alpha Algorithm 135
6.2.2 Cyborg Beta Algorithm 136
6.2.3 Cyborg Gamma Algorithm 138
6.3 Swarm Intelligence Approaches to Swarm Cyborg 139
6.4 Swarm Cyborg Applications 140
6.4.1 Challenges and Issues 145
6.5 Conclusion 146
7 Immune-Inspired Swarm Cybernetic Systems 149
7.1 Introduction 149
7.1.1 Understanding the Problem Domain in Swarm
Cybernetic Systems 150
