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Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilitates an understanding of complicated phenomena that cannot be solved analytically. Agent-Based Modeling and Simulation with Swarm provides the methodology for a multi-agent-based modeling approach that integrates computational techniques such as artificial life, cellular automata, and bio-inspired optimization.
Each chapter gives an overview of the problem, explores state-of-the-art technology in the field, and discusses multi-agent frameworks. The author describes step by step how to assemble algorithms for generating a simulation model, program, method for visualization, and further research tasks. While the book employs the commonly used Swarm system, readers can model and develop the simulations with their own simulator. To encourage hands-on exploration of emergent systems, Swarm-based software and source codes are available for download from the author’s website.
A thorough overview of multi-agent simulation and supporting tools, this book shows how this type of simulation is used to acquire an understanding of complex systems and artificial life. It carefully explains how to construct a simulation program for various applications.
Introduction
What is simulation?
Simulation of intelligence
Criticism of simulation
Swarm and the Santa Fe Institute
Evolutionary Methods and Evolutionary Computation
What is evolutionary computation?
What are genetic algorithms?
What is genetic programming?
What is interactive evolutionary computation?
Multi-Agent Simulation Based on Swarm
Overview of Swarm
Tutorial
Evolutionary Simulation
Simulation of sexual selection
Swarm-based simulation of sexual selection
Simulation of the prisoner’s dilemma
Evolving artificial creatures and artificial life
Ant Colony-Based Simulation
Collective behaviors of ants
Swarm simulation of the pheromone trails of ants
Ant colony optimization (ACO)
Ant-clustering algorithms
Swarm-based simulation of ant-clustering
Ant colony-based approach to the network routing problem
Ant-based job separation
Emergent cooperation of army ants
Particle Swarm Simulation
Boids and flocking behaviors
Simulating boids with Swarm
Swarm chemistry
PSO: particle swarm optimization
ABC algorithm
BUGS: a bug-based search strategy
BUGS in Swarm
Cellular Automata Simulation
Game of life
Conway class with Swarm
Program that replicates itself
Simulating forest fires with Swarm
Segregation model simulation with Swarm
Lattice gas automaton
Turing model and morphogenesis simulation
Simulating percolation with Swarm
Silicon traffic and its control
The world of Sugarscape
Conclusion
Appendix A: GUI Systems and Source Code
Appendix B: Installing Swarm
References
Index
Swarm Index
Name Index
"The book is very readable and contains great illustrations. Each chapter summarizes the problems addressed and the current state of the art, and eases into a detailed discussion on why agent-based modeling sheds new light on the topic at hand. The author performs a difficult task gracefully: he explains just enough for the reader to grasp the essence of a problem, while the bulk of the chapter is spent demonstrating the relevance of agent-based modeling in addressing it."
—Klaus K. Obermeier, PhD, in Computing Reviews
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