dPSO-Vis: Topology-based Visualization of Discrete Particle Swarm Optimization
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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and Blackwell Publishing Ltd.
Abstract
Particle swarm optimization (PSO) is a metaheuristic that has been applied successfully to many continuous and combinatorial optimization problems, e.g., in the fields of economics, engineering, and natural sciences. In PSO, a swarm of particles moves within a search space in order to find an optimal solution. Unfortunately, it is hard to understand in detail why and how changes in the design of PSO algorithms affect the optimization behavior. Visualizing the particle states could provide substantially better insight into PSO algorithms. Though in case of combinatorial optimization problems, it often raises the problem of illustrating the states within the discrete search space that cannot be embedded spatially. We propose a visualization approach to depict the optimization problem topologically using a landscape metaphor. This visualization is augmented by an illustration of the time-dependent states of the particles. Thus, the user of dPSO-Vis is able to analyze the swarm's behavior within the search space. In principle, our method can be used for any optimization algorithm where a swarm of individuals searches within a discrete search space. Our approach is verified with a case study for the PSO algorithm HelixPSO that predicts the secondary structure of RNA molecules.
Description
@article{:10.1111/cgf.12122,
journal = {Computer Graphics Forum},
title = {{dPSO-Vis: Topology-based Visualization of Discrete Particle Swarm Optimization}},
author = {Volke, Sebastian and Middendorf, Martin and Hlawitschka, Mario and Kasten, Jens and Zeckzer, Dirk and Scheuermann, Gerik},
year = {2013},
publisher = {The Eurographics Association and Blackwell Publishing Ltd.},
ISSN = {1467-8659},
DOI = {/10.1111/cgf.12122}
}