Focusing on the algorithmic implementation of models of swarmbehavior, this book:
- Examines how social network structures are used to exchangeinformation among individuals, and how the aggregate behaviour ofthese individuals forms a powerful organism.
- Introduces a compact summary of the formal theory ofoptimisation.
- Outlines paradigms with relations to SI, including geneticalgorithms, evolutionary programming, evolutionary strategies,cultural algorithms and co-evolution.
- Looks at the choreographic movements of birds in a flock as abasis for the Particle Swarm Optimization (PSO) models, andprovides an extensive treatment of different classes of PSOmodels.
- Shows how the behaviour of ants can be used to implement AntColony Optimization (ACO) algorithms to solve real-world problemsincluding routing optimization, structure optimization, data miningand data clustering.
- Considers different classes of optimization problems, includingmulti-objective optimization, dynamic environments, discrete andcontinuous search spaces, constrained optimization, andniching.
- Includes an accompanying website containing Java classes andimplementations of the different algorithms that can be used totest PSO and ACO algorithms: http://si.cs.up.ac.za
The interdisciplinary nature of this field will makeFundamentals of Computational Swarm Intelligence anessential resource for readers with diverse backgrounds.Â Inaddition, it will be an excellent reference for computerscientists, practitioners in business or industry and researchersinvolved in the analysis, design and simulation of multibodysystems.Â Advanced undergraduates and graduate students inartificial intelligence, collective intelligence and engineeringwill also find this book an invaluable tool.
About: Fundamentals of Computational Swarm Intelligence provides acomprehensive introduction to the new computational paradigm ofSwarm Intelligence (SI), a field that emerged from biologicalresearch, and is now picking up momentum within the computationalresearch community.
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