Mathematica 9 is now available

Wolfram Library Archive


Courseware Demos MathSource Technical Notes
All Collections Articles Books Conference Proceedings
Title

Particle Swarm Optimization: An Exploration Kit for Evolutionary Optimization
Authors

N. Khemka
Christian J. Jacob
Organization: University of Calgary
Department: Computer Science
Conference

2004 International Mathematica Symposium
Conference location

Banff, Canada
Description

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.
Subjects

*Applied Mathematics > Optimization
*Social Science
Keywords

Particle Swarm Optimization, PSO, evolution-based search, parameter optimization, multi-dimensional search spaces, social behaviour patterns, global dynamic behaviour patterns, continuous search problems
Related items

*New Ideas in Symbolic Computation: Proceedings of the 6th International Mathematica Symposium   [in Books]