Swarm Intelligence From Natural to Artificial Systems 1st edition by Eric Bonabeau, Guy Theraulaz, Marco Dorigo ISBN 0195131592 978-0195131598

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ISBN 10: 0195131592
ISBN 13: 978-0195131598
Author:  Eric Bonabeau, Guy Theraulaz, Marco Dorigo 

Social insects–ants, bees, termites, and wasps–can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve–finding food, dividing labor among nestmates, building nests, responding to external challenges–have important counterparts in engineering and computer science.

This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.

Swarm Intelligence : From Natural to Artificial Systems 1st Table of contents:

Preface

• Introduction to Swarm Intelligence
• Objectives of the Book
• Target Audience and Structure
• Overview of Swarm Intelligence Concepts
• Importance and Relevance in AI and Robotics

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Part I: Foundations of Swarm Intelligence

1. Introduction to Swarm Intelligence

   • What is Swarm Intelligence?
   • Biological Inspiration: Social Insects, Flocking, and Schooling
   • Swarm Behavior: Emergence, Self-Organization, and Adaptation
   • Historical Overview of Swarm Intelligence Concepts
   • Key Characteristics of Swarm Systems

2. Biological Systems and Swarm Intelligence

   • Collective Behavior in Nature: Ants, Bees, Birds, and Fish
   • Decentralized Control: No Central Leader in Swarms
   • Task Allocation and Cooperation in Animal Swarms
   • Swarm Communication and Simple Rules Leading to Complex Behavior
   • Case Studies: Ant Colonies, Bee Swarms, Flocking Birds

3. Mathematical Models of Swarm Behavior

   • Modelling Individual and Collective Behavior
   • Stigmergy: Indirect Communication through the Environment
   • The Role of Probabilities and Randomness in Swarm Dynamics
   • Differential Equations and Agent-based Models
   • Computational Models for Swarm Systems

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Part II: Artificial Swarm Intelligence Systems

4. Swarm Intelligence in Artificial Systems

   • From Biology to Artificial Systems: Key Concepts and Adaptations
   • Swarm Robotics and Multi-Agent Systems
   • Swarm Intelligence in Computational Problem Solving
   • Optimization and Search Algorithms Inspired by Swarms
   • The Role of Feedback and Local Interactions in Artificial Swarms

5. Ant Colony Optimization (ACO)

   • Introduction to Ant Colony Optimization
   • The ACO Algorithm: Basic Concepts and Steps
   • Applications of ACO in Optimization Problems (e.g., TSP, Network Routing)
   • Variants and Improvements of the ACO Algorithm
   • Performance Evaluation and Practical Considerations

6. Particle Swarm Optimization (PSO)

   • Introduction to Particle Swarm Optimization
   • Particle Movement and Swarm Dynamics in PSO
   • Applications of PSO in Continuous and Discrete Optimization
   • Variants of PSO: Constrained, Multi-objective, and Hybrid PSO
   • Fine-tuning PSO Parameters and Performance Enhancement

7. Artificial Bee Colony (ABC) Algorithm

   • The Biological Inspiration Behind the ABC Algorithm
   • Understanding the Foraging Behavior of Bees
   • ABC Algorithm Steps and Variants
   • Applications of ABC in Engineering and Optimization Problems
   • ABC vs. Other Swarm Algorithms

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Part III: Advanced Topics in Swarm Intelligence

8. Swarm Intelligence for Multi-Agent Systems

   • The Role of Swarm Intelligence in Distributed Problem Solving
   • Cooperative and Competitive Behavior in Multi-Agent Systems
   • Coordination and Negotiation Among Agents in a Swarm
   • Conflict Resolution and Consensus in Swarm Systems
   • Case Studies of Swarm Intelligence in Multi-Agent Applications

9. Swarm Intelligence in Robotics

   • Swarm Robotics: Principles and Techniques
   • Collective Robot Behavior and Self-organization
   • Applications of Swarm Robotics in Search-and-Rescue, Surveillance, and Exploration
   • Communication and Coordination in Swarm Robotics
   • Real-World Challenges and Future Directions

10. Swarm Intelligence in Machine Learning and Data Mining

• Swarm Algorithms for Feature Selection and Classification
• Hybrid Models: Swarm Intelligence and Neural Networks
• Swarm Intelligence in Clustering and Data Clustering
• Adaptive Systems and Learning via Swarm Algorithms
• Applications in Big Data and Predictive Analytics

11. Multi-Objective Optimization with Swarm Intelligence

• Introduction to Multi-Objective Optimization
• Swarm Algorithms for Solving Multi-Objective Problems
• Pareto Efficiency and Trade-offs in Swarm-Based Solutions
• Applications in Engineering Design, Scheduling, and Resource Allocation
• Evolutionary Approaches to Multi-Objective Optimization

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Part IV: Applications of Swarm Intelligence

12. Swarm Intelligence in Network Design and Management

• Applications in Communication and Sensor Networks
• Swarm Algorithms for Network Routing and Topology Design
• Energy-Efficient Swarm-Based Network Management
• Robustness and Scalability in Swarm Network Systems
• Case Studies in Wireless Sensor Networks and IoT

13. Swarm Intelligence in Artificial Life and Autonomous Systems

• Swarm Intelligence in Simulating Artificial Life Forms
• Autonomous Systems: Swarm Behavior for Exploration and Decision Making
• Swarm-Based AI for Self-Healing Systems and Fault Tolerance
• Real-Time Decision Making and Adaptation in Autonomous Swarms
• Examples of Autonomous Swarm Systems in Industry and Research

14. Swarm Intelligence in Optimization and Engineering

• Industrial Applications: Manufacturing, Logistics, and Supply Chain Optimization
• Use of Swarm Intelligence in Structural Design and Simulation
• Swarm Algorithms in Electrical and Mechanical Systems Design
• Applications in Aerospace, Automotive, and Environmental Engineering
• Swarm Intelligence for Energy and Sustainability

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Part V: Future Directions and Challenges

15. Challenges in Swarm Intelligence Research and Applications

• Scalability and Robustness of Swarm Systems
• Dealing with Uncertainty, Noise, and Dynamic Environments
• Safety and Security Issues in Swarm-Based Systems
• Ethical Considerations in Autonomous Swarm Systems
• Open Problems and Research Opportunities

16. Future Trends in Swarm Intelligence

• The Role of Artificial Intelligence and Deep Learning in Swarm Systems
• Quantum Swarm Intelligence and its Potential
• Hybrid Systems: Combining Swarm Intelligence with Other AI Paradigms
• Future Applications in Smart Cities, Healthcare, and Beyond
• Long-Term Vision: Swarm Intelligence and the Future of Autonomous Systems

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References

• Academic Papers, Research Articles, and Books for Further Reading

Index

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