Swarm Intelligence

Book description

Traditional methods for creating intelligent computational systems have
privileged private "internal" cognitive and computational processes. In
contrast, Swarm Intelligence argues that human
intelligence derives from the interactions of individuals in a social world
and further, that this model of intelligence can be effectively applied to
artificially intelligent systems. The authors first present the foundations of
this new approach through an extensive review of the critical literature in
social psychology, cognitive science, and evolutionary computation. They
then show in detail how these theories and models apply to a new
computational intelligence methodology—particle swarms—which focuses
on adaptation as the key behavior of intelligent systems. Drilling down
still further, the authors describe the practical benefits of applying particle
swarm optimization to a range of engineering problems. Developed by
the authors, this algorithm is an extension of cellular automata and
provides a powerful optimization, learning, and problem solving method.


This important book presents valuable new insights by exploring the
boundaries shared by cognitive science, social psychology, artificial life,
artificial intelligence, and evolutionary computation and by applying these
insights to the solving of difficult engineering problems. Researchers and
graduate students in any of these disciplines will find the material
intriguing, provocative, and revealing as will the curious and savvy
computing professional.

* Places particle swarms within the larger context of intelligent
adaptive behavior and evolutionary computation.
* Describes recent results of experiments with the particle swarm
optimization (PSO) algorithm
* Includes a basic overview of statistics to ensure readers can
properly analyze the results of their own experiments using the
algorithm.
* Support software which can be downloaded from the publishers
website, includes a Java PSO applet, C and Visual Basic source
code.

Table of contents

  1. Preface
    1. A Thumbnail Sketch of Particle Swarm Optimization
    2. What This Book Is, and Is Not, About
    3. Assertions
    4. Organization of the Book
    5. Software
    6. Definitions
    7. Acknowledgments
  2. Part One Foundations
    1. 1 Models and Concepts of Life and Intelligence
      1. The Mechanics of Life and Thought
      2. Stochastic Adaptation: Is Anything Ever Really Random?
      3. The "Two Great Stochastic Systems"
      4. The Game of Life: Emergence in Complex Systems
        1. The Game of Life
        2. Emergence
      5. Cellular Automata and the Edge of Chaos (1/2)
      6. Cellular Automata and the Edge of Chaos (2/2)
      7. Artificial Life in Computer Programs
      8. Intelligence: Good Minds in People and Machines
        1. Intelligence in People: The Boring Criterion
        2. Intelligence in Machines: The Turing Criterion
    2. 2 Symbols, Connections, and Optimization by Trial and Error
      1. Symbols in Trees and Networks (1/3)
      2. Symbols in Trees and Networks (2/3)
      3. Symbols in Trees and Networks (3/3)
      4. Problem Solving and Optimization (1/2)
      5. Problem Solving and Optimization (2/2)
        1. A Super-Simple Optimization Problem
        2. Three Spaces of Optimization
        3. Fitness Landscapes
      6. High-Dimensional Cognitive Space and Word Meanings
      7. Two Factors of Complexity:
      8. Landscapes
      9. Combinatorial Optimization
      10. Binary Optimization (1/2)
      11. Binary Optimization (2/2)
        1. Random and Greedy Searches
        2. Hill Climbing
        3. Simulated Annealing
        4. Binary and Gray Coding
        5. Step Sizes and Granularity
      12. Optimizing with Real Numbers
      13. Summary
    3. 3 On Our Nonexistence as Entities: The Social Organism
      1. Views of Evolution (1/3)
      2. Views of Evolution (2/3)
      3. Views of Evolution (3/3)
        1. Gaia: The Living Earth
        2. Differential Selection
        3. Our Microscopic Masters?
        4. Looking for the Right Zoom Angle
      4. Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization (1/5)
      5. Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization (2/5)
      6. Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization (3/5)
      7. Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization (4/5)
      8. Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization (5/5)
        1. Accomplishments of the Social Insects
        2. Optimizing with Simulated Ants: Computational
        3. Swarm Intelligence
        4. Staying Together but Not Colliding: Flocks, Herds,
        5. and Schools
      9. Robot Societies (1/2)
      10. Robot Societies (2/2)
      11. Shallow Understanding
      12. Agency
      13. Summary
    4. 4 Evolutionary Computation Theory and Paradigms
      1. Introduction
      2. Evolutionary Computation History
        1. The Four Areas of Evolutionary Computation
        2. Genetic Algorithms
        3. Evolutionary Programming
        4. Evolution Strategies
        5. Genetic Programming
        6. Toward Unification
      3. Evolutionary Computation Overview
        1. EC Paradigm Attributes
        2. Implementation
      4. Genetic Algorithms (1/4)
      5. Genetic Algorithms (2/4)
      6. Genetic Algorithms (3/4)
      7. Genetic Algorithms (4/4)
        1. An Overview
        2. A Simple GA Example Problem
        3. A Review of GA Operations
        4. Schemata and the Schema Theorem
        5. Final Comments on Genetic Algorithms
      8. Evolutionary Programming (1/2)
      9. Evolutionary Programming (2/2)
        1. The Evolutionary Programming Procedure
        2. Finite State Machine Evolution
        3. Function Optimization
        4. Final Comments
      10. Evolution Strategies (1/2)
      11. Evolution Strategies (2/2)
        1. Mutation
        2. Recombination
        3. Selection
      12. Genetic Programming (1/2)
      13. Genetic Programming (2/2)
      14. Summary
    5. 5 Humans — Actual, Imagined, and Implied
      1. Studying Minds (1/3)
      2. Studying Minds (2/3)
      3. Studying Minds (3/3)
        1. The Fall of the Behaviorist Empire
        2. The Cognitive Revolution
        3. Bandura’s Social Learning Paradigm
      4. Social Psychology (1/2)
      5. Social Psychology (2/2)
        1. Lewin’s Field Theory
        2. Norms, Conformity, and Social Influence
        3. Sociocognition
      6. Simulating Social Influence (1/3)
      7. Simulating Social Influence (2/3)
      8. Simulating Social Influence (3/3)
        1. Paradigm Shifts in Cognitive Science
        2. The Evolution of Cooperation
        3. Explanatory Coherence
        4. Networks in Groups
      9. Culture in Theory and Practice (1/7)
      10. Culture in Theory and Practice (2/7)
      11. Culture in Theory and Practice (3/7)
      12. Culture in Theory and Practice (4/7)
      13. Culture in Theory and Practice (5/7)
      14. Culture in Theory and Practice (6/7)
      15. Culture in Theory and Practice (7/7)
        1. Coordination Games
        2. The El Farol Problem
        3. Sugarscape
        4. Tesfatsion’s ACE
        5. Picker’s Competing-Norms Model
        6. Latan'’s Dynamic Social Impact Theory
        7. Boyd and Richerson’s Evolutionary Culture Model
        8. Memetics
        9. Memetic Algorithms
        10. Cultural Algorithms
        11. Convergence of Basic and Applied Research
      16. Culture-and Life without It
      17. Summary
    6. 6 Thinking Is Social
      1. Introduction
        1. Adaptation on Three Levels
        2. The Adaptive Culture Model
      2. Axelrod’s Culture Model
      3. Experiment One: Similarity in Axelrod’s Model
      4. Experiment Two: Optimization of an Arbitrary Function
      5. Experiment Three: A Slightly Harder and More Interesting Function
      6. Experiment Four: A Hard Function
      7. Experiment Five: Parallel Constraint Satisfaction (1/2)
      8. Experiment Five: Parallel Constraint Satisfaction (2/2)
      9. Experiment Six: Symbol Processing
      10. Discussion
      11. Summary
  3. Part Two The Particle Swarm and Collective Intelligence
    1. 7 The Particle Swarm
      1. Sociocognitive Underpinnings: Evaluate, Compare, and Imitate
        1. Evaluate
        2. Compare
        3. Imitate
      2. A Model of Binary Decision (1/4)
      3. A Model of Binary Decision (2/4)
      4. A Model of Binary Decision (3/4)
      5. A Model of Binary Decision (4/4)
        1. Testing the Binary Algorithm with the De Jong Test Suite
        2. No Free Lunch
        3. Multimodality
        4. Minds as Parallel Constraint Satisfaction Networks
        5. in Cultures
      6. The Particle Swarm in Continuous Numbers (1/2)
      7. The Particle Swarm in Continuous Numbers (2/2)
        1. The Particle Swarm in Real-Number Space
        2. Pseudocode for Particle Swarm Optimization in
        3. Continuous Numbers
        4. Implementation Issues
        5. An Example: Particle Swarm Optimization of Neural
        6. Net Weights
        7. A Real-World Application
      8. The Hybrid Particle Swarm
      9. Science as Collaborative Search
      10. Emergent Culture, Immergent Intelligence
      11. Summary
    2. 8 Variations and Comparisons
      1. Variations of the Particle Swarm Paradigm (1/7)
      2. Variations of the Particle Swarm Paradigm (2/7)
      3. Variations of the Particle Swarm Paradigm (3/7)
      4. Variations of the Particle Swarm Paradigm (4/7)
      5. Variations of the Particle Swarm Paradigm (5/7)
      6. Variations of the Particle Swarm Paradigm (6/7)
      7. Variations of the Particle Swarm Paradigm (7/7)
        1. Parameter Selection
        2. Controlling the Explosion
        3. Particle Interactions
        4. Neighborhood Topology
        5. Substituting Cluster Centers for Previous Bests
        6. Adding Selection to Particle Swarms
        7. Comparing Inertia Weights and Constriction Factors
        8. Asymmetric Initialization
        9. Some Thoughts on Variations
      8. Are Particle Swarms Really a Kind of Evolutionary Algorithm? (1/2)
      9. Are Particle Swarms Really a Kind of Evolutionary Algorithm? (2/2)
        1. Evolution beyond Darwin
        2. Selection and Self-Organization
        3. Ergodicity: Where Can It Get from Here?
        4. Convergence of Evolutionary Computation and
        5. Particle Swarms
      10. Summary
    3. 9 Applications
      1. Evolving Neural Networks with Particle Swarms (1/3)
      2. Evolving Neural Networks with Particle Swarms (2/3)
      3. Evolving Neural Networks with Particle Swarms (3/3)
        1. Review of Previous Work
        2. Advantages and Disadvantages of Previous Approaches
        3. The Particle Swarm Optimization Implementation
        4. Used Here
        5. Implementing Neural Network Evolution
        6. An Example Application
        7. Conclusions
      4. Human Tremor Analysis (1/2)
      5. Human Tremor Analysis (2/2)
        1. Data Acquisition Using Actigraphy
        2. Data Preprocessing
        3. Analysis with Particle Swarm Optimization
        4. Summary
      6. Other Applications
        1. Computer Numerically Controlled Milling Optimization
        2. Ingredient Mix Optimization
        3. Reactive Power and Voltage Control
        4. Battery Pack State-of-Charge Estimation
      7. Summary
    4. 10 Implications and Speculations
      1. Introduction
      2. Assertions
      3. Up from Social Learning: Bandura
        1. Information and Motivation
        2. Vicarious versus Direct Experience
        3. The Spread of Influence
      4. Machine Adaptation
      5. Learning or Adaptation?
      6. Cellular Automata
      7. Down from Culture
      8. Soft Computing
      9. Interaction within Small Groups: Group Polarization
      10. Informational and Normative Social Influence
      11. Self-Esteem
      12. Self-Attribution and Social Illusion
      13. Summary
    5. 11 And in Conclusion . . . (1/2)
    6. 11 And in Conclusion . . . (2/2)
    7. Appendix A Statistics for Swarmers
      1. Descriptive Statistics
      2. Inferential Statistics
      3. Confidence Intervals
      4. Student’s
      5. Test
      6. One-Way Analysis of Variance
      7. Factorial ANOVA
      8. Multivariate ANOVA
      9. Regression Analysis
      10. The Chi-Square Test of Independence
      11. Experimental Design
    8. Appendix B Genetic Algorithm Implementation
      1. The Run File
      2. Recompiling
      3. Running the Program (1/13)
      4. Running the Program (2/13)
      5. Running the Program (3/13)
      6. Running the Program (4/13)
      7. Running the Program (5/13)
      8. Running the Program (6/13)
      9. Running the Program (7/13)
      10. Running the Program (8/13)
      11. Running the Program (9/13)
      12. Running the Program (10/13)
      13. Running the Program (11/13)
      14. Running the Program (12/13)
      15. Running the Program (13/13)

Product information

  • Title: Swarm Intelligence
  • Author(s): Russell C. Eberhart, Yuhui Shi, James Kennedy
  • Release date: April 2001
  • Publisher(s): Morgan Kaufmann
  • ISBN: 9780080518268