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Evolutionary & Swarm Design
 
Sunday, August 1, 2004
Particle Swarm Optimization in Mathematica:
An Exploration Kit for Evolutionary Optimization
 
Christian Jacob, Namrata Khemka   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. In: International Mathematica Symposium 2004 IMS2004PSO.nb PSO-Experiments.nbA2AFD11C-BF1D-47CC-9930-F85A62739B2D_files/IMS2004PSO.nbA2AFD11C-BF1D-47CC-9930-F85A62739B2D_files/PSO-Experiments.nbshapeimage_3_link_0shapeimage_3_link_1
Particle Swarm Optimization