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Submit a ProposalSwarm Intelligence
 | An Approach from Natural to Artificial By Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal and Avadhesh Kumar Series: Concise Introductions to AI and Data Science Copyright: 2023 | Status: Published ISBN: 9781119865063 | Hardcover | 242 pages Price: $165 USD  |
One Line Description
This important authored book presents valuable new insights by
exploring the boundaries shared by cognitive science, social psychology,
artificial life, artificial intelligence, and evolutionary computation by
applying these insights to solving complex engineering problems.
Audience
The book is directed to researchers, practicing engineers, and students in computational intelligence who are interested in enhancing their knowledge of techniques and swarm intelligence.
The book is directed to researchers, practicing engineers, and students in computational intelligence who are interested in enhancing their knowledge of techniques and swarm intelligence.
Description
Motivated by the capability of the biologically inspired algorithms, “Swarm Intelligence: An Approach from Natural to Artificial” focuses on ant, cat, crow, elephant, grasshopper, water wave and whale optimization, swarm cyborg and particle swarm optimization, and presents recent developments and applications concerning optimization with swarm intelligence techniques. The goal of the book is to offer a wide spectrum of sample works developed in leading research throughout the world about innovative methodologies of swarm intelligence and foundations of engineering swarm intelligent systems; as well as applications and interesting experiences using particle swarm optimization, which is at the heart of computational intelligence.
Discussed in the book are applications of various swarm intelligence models to operational planning of energy plants, modeling, and control of robots, organic computing, techniques of cloud services, bioinspired optimization, routing protocols for next-generation networks inspired by collective behaviors of insect societies and cybernetic organisms.
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Motivated by the capability of the biologically inspired algorithms, “Swarm Intelligence: An Approach from Natural to Artificial” focuses on ant, cat, crow, elephant, grasshopper, water wave and whale optimization, swarm cyborg and particle swarm optimization, and presents recent developments and applications concerning optimization with swarm intelligence techniques. The goal of the book is to offer a wide spectrum of sample works developed in leading research throughout the world about innovative methodologies of swarm intelligence and foundations of engineering swarm intelligent systems; as well as applications and interesting experiences using particle swarm optimization, which is at the heart of computational intelligence.
Discussed in the book are applications of various swarm intelligence models to operational planning of energy plants, modeling, and control of robots, organic computing, techniques of cloud services, bioinspired optimization, routing protocols for next-generation networks inspired by collective behaviors of insect societies and cybernetic organisms.
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Author / Editor Details
Kuldeep Singh Kaswan, PhD, is working in the School of Computing Science & Engineering, Galgotias University, Uttar Pradesh, India. He received his PhD in computer science from Banasthali Vidyapith, Rajasthan, and D. Engg. from Dana Brain Health Institute, Iran. His research interests are in brain-computer interface, cyborg, and data sciences.
Kuldeep Singh Kaswan, PhD, is working in the School of Computing Science & Engineering, Galgotias University, Uttar Pradesh, India. He received his PhD in computer science from Banasthali Vidyapith, Rajasthan, and D. Engg. from Dana Brain Health Institute, Iran. His research interests are in brain-computer interface, cyborg, and data sciences.
Jagjit Singh Dhatterwal, PhD, is an associate professor in the Department of Artificial Intelligence & Data Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India. He completed his doctorate in computer science from Mewar University, Rajasthan, India. He has numerous publications in international/national journals and conferences.
Avadhesh Kumar, PhD, is Pro Vice-Chancellor at Galgotias University, India. He obtained his doctorate in computer science with a specialization in software engineering from Thapar University, Patiala, Punjab. He has more than 22 years of teaching and research experience and has published more than 40 research papers in SCI international journals/conferences. His research areas are aspect-oriented programming (AOP), software metrics, software quality, component-based software development (CBSD), artificial intelligence, and autonomic computing.
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Table of Contents
Preface
1. Introduction of Swarm Intelligence
1.1 Introduction to Swarm Behavior
1.1.1 Individual vs. Collective Behaviors
1.2 Concepts of Swarm Intelligence
1.3 Particle Swarm Optimization (PSO)
1.3.1 Main Concept of PSO
1.4 Meaning of Swarm Intelligence
1.5 What Is Swarm Intelligence?
1.5.1 Types of Communication Between Swarm Agents
1.5.2 Examples of Swarm Intelligence
1.6 History of Swarm Intelligence
1.7 Taxonomy of Swarm Intelligence
1.8 Properties of Swarm Intelligence
1.8.1 Models of Swarm Behavior
1.8.2 Self-Propelled Particles
1.9 Design Patterns in Cyborg Swarm
1.9.1 Design Pattern Creation
1.9.2 Design Pattern Primitives and Their Representation
1.10 Design Patterns Updating in Cyborg
1.10.1 Behaviors and Data Structures
1.10.2 Basics of Cyborg Swarming
1.10.3 Information Exchange at Worksites
1.10.4 Information Exchange Center
1.10.5 Working Features of Cyborg
1.10.6 Highest Utility of Cyborg
1.10.7 Gain Extra Reward
1.11 Property of Design Cyborg
1.12 Extending the Design of Cyborg
1.12.1 Information Storage in Cyborg
1.12.2 Information Exchange Any Time
1.12.3 The New Design Pattern Rules in Cyborg
1.13 Bee-Inspired Cyborg
1.14 Conclusion
2. Foundation of Swarm Intelligence
2.1 Introduction
2.2 Concepts of Life and Intelligence
2.2.1 Intelligence: Good Minds in People and Machines
2.2.2 Intelligence in People: The Boring Criterion
2.2.3 Intelligence in Machines: The Turing Criterion
2.3 Symbols, Connections, and Optimization by Trial and Error
2.3.1 Problem Solving and Optimization
2.3.2 A Super-Simple Optimization Problem
2.3.3 Three Spaces of Optimization
2.3.4 High-Dimensional Cognitive Space and Word Meanings
2.4 The Social Organism
2.4.1 Flocks, Herds, Schools and Swarms: Social Behavior as Optimization
2.4.2 Accomplishments of the Social Insects
2.4.3 Optimizing with Simulated Ants: Computational Swarm Intelligence
2.5 Evolutionary Computation Theory and Paradigms
2.5.1 The Four Areas of Evolutionary Computation
2.5.2 Evolutionary Computation Overview
2.5.3 Evolutionary Computing Technologies
2.6 Humans – Actual, Imagined, and Implied
2.6.1 The Fall of the Behaviorist Empire
2.7 Thinking is Social
2.7.1 Adaptation on Three Levels
2.8 Conclusion
3. The Particle Swarm and Collective Intelligence
3.1 The Particle Swarm and Collective Intelligence
3.1.1 Socio-Cognitive Underpinnings: Evaluate, Compare, and Imitate
3.1.2 A Model of Binary Decision
3.1.3 The Particle Swarm in Continuous Numbers
3.1.4 Pseudocode for Particle Swarm Optimization in Continuous Numbers
3.2 Variations and Comparisons
3.2.1 Variations of the Particle Swarm Paradigm
3.2.2 Parameter Selection
3.2.3 Vmax
3.2.4 Controlling the Explosion
3.2.5 Simplest Constriction
3.2.6 Neighborhood Topology
3.2.7 Sociometric of the Particle Swarm
3.2.8 Selection and Self-Organization
3.2.9 Ergodicity: Where Can It Go from Here?
3.2.10 Convergence of Evolutionary Computation and Particle Swarms
3.3 Implications and Speculations
3.3.1 Assertions in Cuckoo Search
3.3.2 Particle Swarms Are a Valuable Soft Intelligence (Machine Learning Intelligent) Approach
3.3.3 Information and Motivation
3.3.4 Vicarious vs. Direct Experience
3.3.5 The Spread of Influence
3.3.6 Machine Adaptation
3.3.7 Learning or Adaptation?
3.4 Conclusion
4. Algorithm of Swarm Intelligence
4.1 Introduction
4.1.1 Methods for Alternate Stages of Model Parameter Reform
4.1.2 Ant Behavior
4.2 Ant Colony Algorithm
4.3 Artificial Bee Colony Optimization
4.3.1 The Artificial Bee Colony
4.4 Cat Swarm Optimization
4.4.1 Original CSO Algorithm
4.4.2 Description of the Global Version of CSO Algorithm
4.4.3 Seeking Mode (Resting)
4.4.4 Tracing Mode (Movement)
4.4.5 Description of the Local Version of CSO Algorithm
4.5 Crow Search Optimization
4.5.1 Original CSA
4.6 Elephant Intelligent Behavior
4.6.1 Elephant Herding Optimization
4.6.2 Position Update of Elephants in a Clan
4.6.3 Pseudocode of EHO Flowchart
4.7 Grasshopper Optimization
4.7.1 Description of the Grasshopper Optimization Algorithm
4.8 Conclusion
5. Novel Swarm Intelligence Optimization Algorithm (SIOA)
5.1 Water Wave Optimization
5.1.1 Objective Function
5.1.2 Power Balance Constraints
5.1.3 Generator Capacity Constraints
5.1.4 Water Wave Optimization Algorithm
5.1.5 Mathematical Model of WWO Algorithm
5.1.6 Implementation of WWO Algorithm for ELD Problem
5.2 Brain Storm Optimization
5.2.1 Multi-Objective Brain Storm Optimization Algorithm
5.2.2 Clustering Strategy
5.2.3 Generation Process
5.2.4 Mutation Operator
5.2.5 Selection Operator
5.2.6 Global Archive
5.3 Whale Optimization Algorithm
5.3.1 Description of the WOA
5.4 Conclusion
6. Swarm Cyborg
6.1 Introduction
6.1.1 Swarm Intelligence Cyborg
6.2 Swarm Cyborg Taxis Algorithms
6.2.1 Cyborg Alpha Algorithm
6.2.2 Cyborg Beta Algorithm
6.2.3 Cyborg Gamma Algorithm
6.3 Swarm Intelligence Approaches to Swarm Cyborg
6.4 Swarm Cyborg Applications
6.4.1 Challenges and Issues
6.5 Conclusion
7. Immune-Inspired Swarm Cybernetic Systems
7.1 Introduction
7.1.1 Understanding the Problem Domain in Swarm Cybernetic Systems
7.1.2 Applying Conceptual Framework in Developing Immune-Inspired Swarm Cybernetic Systems Solutions
7.2 Reflections on the Development of Immune-Inspired Solution for Swarm Cybernetic Systems
7.2.1 Reflections on the Cyborg Conceptual Framework
7.2.2 Immunology and Probes
7.2.3 Simplifying Computational Model and Algorithm Framework/Principle
7.2.4 Reflections on Swarm Cybernetic Systems
7.3 Cyborg Static Environment
7.4 Cyborg Swarm Performance
7.4.1 Solitary Cyborg Swarms
7.4.2 Local Cyborg Broadcasters
7.4.3 Cyborg Bee Swarms
7.4.4 The Performance of Swarm Cyborgs
7.5 Information Flow Analysis in Cyborgs
7.5.1 Cyborg Scouting Behavior
7.5.2 Information Gaining by Cyborg
7.5.3 Information Gain Rate of Cyborgs
7.5.4 Evaluation of Information Flow in Cyborgs
7.6 Cost Analysis of Cyborgs
7.6.1 The Cyborg Work Cycle
7.6.2 Uncertainty Cost of Cyborgs
7.6.3 Cyborg Opportunity Cost
7.6.4 Costs and Rewards Obtained by Cyborgs
7.7 Cyborg Swarm Environment
7.7.1 Cyborg Scouting Efficiency
7.7.2 Cyborg Information Gain Rate
7.7.3 Swarm Cyborg Costs
7.7.4 Solitary Swarm Cyborg Costs
7.7.5 Information-Cost-Reward Framework
7.8 Conclusion
8. Application of Swarm Intelligence
8.1 Swarm Intelligence Robotics
8.1.1 What is Swarm Robotics?
8.1.2 System-Level Properties
8.1.3 Coordination Mechanisms
8.2 An Agent-Based Approach to Self-Organized Production
8.2.1 Ingredients Model
8.3 Organic Computing and Swarm Intelligence
8.3.1 Organic Computing Systems
8.4 Swarm Intelligence Techniques for Cloud Services
8.4.1 Context
8.4.2 Model Formulation
8.4.3 Decision Variable
8.4.4 Objective Functions
8.4.5 Solution Evaluation
8.4.6 Genetic Algorithm (GA)
8.4.7 Particle Swarm Optimization (PSO)
8.4.8 Harmony Search (HS)
8.5 Routing Protocols for Next-Generation Networks Inspired by Collective Behaviors of Insect Societies
8.5.1 Classification Features of Network Routing Protocols
8.5.2 Nearest Neighbor Behavior in Ant Colonies and the ACO Metaheuristic to Network Routing Protocols Inspired by Insect Societies
8.5.3 Useful Ideas from Honeybee Colonies
8.5.4 Colony and Workers Recruitment Communications
8.5.5 Stochastic Food Site Selection
8.6 Swarm Intelligence in Data Mining
8.6.1 Steps of Knowledge Discovery
8.7 Swarm Intelligence and Knowledge Discovery
8.8 Ant Colony Optimization and Data Mining
8.9 Conclusion
References
Index
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Preface
1. Introduction of Swarm Intelligence
1.1 Introduction to Swarm Behavior
1.1.1 Individual vs. Collective Behaviors
1.2 Concepts of Swarm Intelligence
1.3 Particle Swarm Optimization (PSO)
1.3.1 Main Concept of PSO
1.4 Meaning of Swarm Intelligence
1.5 What Is Swarm Intelligence?
1.5.1 Types of Communication Between Swarm Agents
1.5.2 Examples of Swarm Intelligence
1.6 History of Swarm Intelligence
1.7 Taxonomy of Swarm Intelligence
1.8 Properties of Swarm Intelligence
1.8.1 Models of Swarm Behavior
1.8.2 Self-Propelled Particles
1.9 Design Patterns in Cyborg Swarm
1.9.1 Design Pattern Creation
1.9.2 Design Pattern Primitives and Their Representation
1.10 Design Patterns Updating in Cyborg
1.10.1 Behaviors and Data Structures
1.10.2 Basics of Cyborg Swarming
1.10.3 Information Exchange at Worksites
1.10.4 Information Exchange Center
1.10.5 Working Features of Cyborg
1.10.6 Highest Utility of Cyborg
1.10.7 Gain Extra Reward
1.11 Property of Design Cyborg
1.12 Extending the Design of Cyborg
1.12.1 Information Storage in Cyborg
1.12.2 Information Exchange Any Time
1.12.3 The New Design Pattern Rules in Cyborg
1.13 Bee-Inspired Cyborg
1.14 Conclusion
2. Foundation of Swarm Intelligence
2.1 Introduction
2.2 Concepts of Life and Intelligence
2.2.1 Intelligence: Good Minds in People and Machines
2.2.2 Intelligence in People: The Boring Criterion
2.2.3 Intelligence in Machines: The Turing Criterion
2.3 Symbols, Connections, and Optimization by Trial and Error
2.3.1 Problem Solving and Optimization
2.3.2 A Super-Simple Optimization Problem
2.3.3 Three Spaces of Optimization
2.3.4 High-Dimensional Cognitive Space and Word Meanings
2.4 The Social Organism
2.4.1 Flocks, Herds, Schools and Swarms: Social Behavior as Optimization
2.4.2 Accomplishments of the Social Insects
2.4.3 Optimizing with Simulated Ants: Computational Swarm Intelligence
2.5 Evolutionary Computation Theory and Paradigms
2.5.1 The Four Areas of Evolutionary Computation
2.5.2 Evolutionary Computation Overview
2.5.3 Evolutionary Computing Technologies
2.6 Humans – Actual, Imagined, and Implied
2.6.1 The Fall of the Behaviorist Empire
2.7 Thinking is Social
2.7.1 Adaptation on Three Levels
2.8 Conclusion
3. The Particle Swarm and Collective Intelligence
3.1 The Particle Swarm and Collective Intelligence
3.1.1 Socio-Cognitive Underpinnings: Evaluate, Compare, and Imitate
3.1.2 A Model of Binary Decision
3.1.3 The Particle Swarm in Continuous Numbers
3.1.4 Pseudocode for Particle Swarm Optimization in Continuous Numbers
3.2 Variations and Comparisons
3.2.1 Variations of the Particle Swarm Paradigm
3.2.2 Parameter Selection
3.2.3 Vmax
3.2.4 Controlling the Explosion
3.2.5 Simplest Constriction
3.2.6 Neighborhood Topology
3.2.7 Sociometric of the Particle Swarm
3.2.8 Selection and Self-Organization
3.2.9 Ergodicity: Where Can It Go from Here?
3.2.10 Convergence of Evolutionary Computation and Particle Swarms
3.3 Implications and Speculations
3.3.1 Assertions in Cuckoo Search
3.3.2 Particle Swarms Are a Valuable Soft Intelligence (Machine Learning Intelligent) Approach
3.3.3 Information and Motivation
3.3.4 Vicarious vs. Direct Experience
3.3.5 The Spread of Influence
3.3.6 Machine Adaptation
3.3.7 Learning or Adaptation?
3.4 Conclusion
4. Algorithm of Swarm Intelligence
4.1 Introduction
4.1.1 Methods for Alternate Stages of Model Parameter Reform
4.1.2 Ant Behavior
4.2 Ant Colony Algorithm
4.3 Artificial Bee Colony Optimization
4.3.1 The Artificial Bee Colony
4.4 Cat Swarm Optimization
4.4.1 Original CSO Algorithm
4.4.2 Description of the Global Version of CSO Algorithm
4.4.3 Seeking Mode (Resting)
4.4.4 Tracing Mode (Movement)
4.4.5 Description of the Local Version of CSO Algorithm
4.5 Crow Search Optimization
4.5.1 Original CSA
4.6 Elephant Intelligent Behavior
4.6.1 Elephant Herding Optimization
4.6.2 Position Update of Elephants in a Clan
4.6.3 Pseudocode of EHO Flowchart
4.7 Grasshopper Optimization
4.7.1 Description of the Grasshopper Optimization Algorithm
4.8 Conclusion
5. Novel Swarm Intelligence Optimization Algorithm (SIOA)
5.1 Water Wave Optimization
5.1.1 Objective Function
5.1.2 Power Balance Constraints
5.1.3 Generator Capacity Constraints
5.1.4 Water Wave Optimization Algorithm
5.1.5 Mathematical Model of WWO Algorithm
5.1.6 Implementation of WWO Algorithm for ELD Problem
5.2 Brain Storm Optimization
5.2.1 Multi-Objective Brain Storm Optimization Algorithm
5.2.2 Clustering Strategy
5.2.3 Generation Process
5.2.4 Mutation Operator
5.2.5 Selection Operator
5.2.6 Global Archive
5.3 Whale Optimization Algorithm
5.3.1 Description of the WOA
5.4 Conclusion
6. Swarm Cyborg
6.1 Introduction
6.1.1 Swarm Intelligence Cyborg
6.2 Swarm Cyborg Taxis Algorithms
6.2.1 Cyborg Alpha Algorithm
6.2.2 Cyborg Beta Algorithm
6.2.3 Cyborg Gamma Algorithm
6.3 Swarm Intelligence Approaches to Swarm Cyborg
6.4 Swarm Cyborg Applications
6.4.1 Challenges and Issues
6.5 Conclusion
7. Immune-Inspired Swarm Cybernetic Systems
7.1 Introduction
7.1.1 Understanding the Problem Domain in Swarm Cybernetic Systems
7.1.2 Applying Conceptual Framework in Developing Immune-Inspired Swarm Cybernetic Systems Solutions
7.2 Reflections on the Development of Immune-Inspired Solution for Swarm Cybernetic Systems
7.2.1 Reflections on the Cyborg Conceptual Framework
7.2.2 Immunology and Probes
7.2.3 Simplifying Computational Model and Algorithm Framework/Principle
7.2.4 Reflections on Swarm Cybernetic Systems
7.3 Cyborg Static Environment
7.4 Cyborg Swarm Performance
7.4.1 Solitary Cyborg Swarms
7.4.2 Local Cyborg Broadcasters
7.4.3 Cyborg Bee Swarms
7.4.4 The Performance of Swarm Cyborgs
7.5 Information Flow Analysis in Cyborgs
7.5.1 Cyborg Scouting Behavior
7.5.2 Information Gaining by Cyborg
7.5.3 Information Gain Rate of Cyborgs
7.5.4 Evaluation of Information Flow in Cyborgs
7.6 Cost Analysis of Cyborgs
7.6.1 The Cyborg Work Cycle
7.6.2 Uncertainty Cost of Cyborgs
7.6.3 Cyborg Opportunity Cost
7.6.4 Costs and Rewards Obtained by Cyborgs
7.7 Cyborg Swarm Environment
7.7.1 Cyborg Scouting Efficiency
7.7.2 Cyborg Information Gain Rate
7.7.3 Swarm Cyborg Costs
7.7.4 Solitary Swarm Cyborg Costs
7.7.5 Information-Cost-Reward Framework
7.8 Conclusion
8. Application of Swarm Intelligence
8.1 Swarm Intelligence Robotics
8.1.1 What is Swarm Robotics?
8.1.2 System-Level Properties
8.1.3 Coordination Mechanisms
8.2 An Agent-Based Approach to Self-Organized Production
8.2.1 Ingredients Model
8.3 Organic Computing and Swarm Intelligence
8.3.1 Organic Computing Systems
8.4 Swarm Intelligence Techniques for Cloud Services
8.4.1 Context
8.4.2 Model Formulation
8.4.3 Decision Variable
8.4.4 Objective Functions
8.4.5 Solution Evaluation
8.4.6 Genetic Algorithm (GA)
8.4.7 Particle Swarm Optimization (PSO)
8.4.8 Harmony Search (HS)
8.5 Routing Protocols for Next-Generation Networks Inspired by Collective Behaviors of Insect Societies
8.5.1 Classification Features of Network Routing Protocols
8.5.2 Nearest Neighbor Behavior in Ant Colonies and the ACO Metaheuristic to Network Routing Protocols Inspired by Insect Societies
8.5.3 Useful Ideas from Honeybee Colonies
8.5.4 Colony and Workers Recruitment Communications
8.5.5 Stochastic Food Site Selection
8.6 Swarm Intelligence in Data Mining
8.6.1 Steps of Knowledge Discovery
8.7 Swarm Intelligence and Knowledge Discovery
8.8 Ant Colony Optimization and Data Mining
8.9 Conclusion
References
Index
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