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dPSO‐Vis: Topology‐based Visualization of Discrete Particle Swarm Optimization
Author(s) -
Volke S.,
Middendorf M.,
Hlawitschka M.,
Kasten J.,
Zeckzer D.,
Scheuermann G.
Publication year - 2013
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12122
Subject(s) - multi swarm optimization , metaheuristic , particle swarm optimization , swarm behaviour , discrete space , computer science , visualization , mathematical optimization , swarm intelligence , combinatorial optimization , derivative free optimization , discrete optimization , optimization problem , continuous optimization , theoretical computer science , algorithm , mathematics , artificial intelligence , mathematical analysis
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.