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Particle Swarm Optimization: An Exploration Kit for Evolutionary Optimization
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| Organization: | University of Calgary |
| Department: | Computer Science |
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2004 International Mathematica Symposium
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Banff, Canada
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Particle Swarm Optimization (PSO) is a relatively new, evolution-based search and optimization technique. PSO algorithms are especially useful for parameter optimization in continuous, multi-dimensional search spaces. PSO is mainly inspired by social behaviour patterns of organisms that live and interact within large groups, such as flocks, swarms, or herds. The connection to search and optimization problems is made by assigning direction vectors and velocities to each point in a multi-dimensional search space, where the 'individuals' interact locally with their neighbours, which leads to global dynamic behaviour (= search) patterns within the overall 'population'. In this paper, we present an implementation of Particle Swarm Optimization in Mathematica. We explain the PSO algorithm in detail and demonstrate its performance on one- and two-dimensional, continuous search problems.
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Particle Swarm Optimization, PSO, evolution-based search, parameter optimization, multi-dimensional search spaces, social behaviour patterns, global dynamic behaviour patterns, continuous search problems
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