Abstract
We describe VISPLORE, a visualization and experimentation environment for population-based search algorithms. Using particle swarm optimization (PSO) as an example, we demonstrate the advantages of an interactive visualization tool for multi-dimensional data. VISPLORE greatly supports the analysis of time dependent data sets, as they are produced by evolutionary optimization algorithms. We demonstrate various multi-dimensional visualization techniques, as built into VISPLORE, which help to understand the dynamics of stochastic search algorithms.
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Khemka, N., Jacob, C. (2010). VISPLORE: Exploring Particle Swarms by Visual Inspection. In: Sarker, R.A., Ray, T. (eds) Agent-Based Evolutionary Search. Adaptation, Learning, and Optimization, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13425-8_12
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DOI: https://doi.org/10.1007/978-3-642-13425-8_12
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