Computing contour trees in all dimensions
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Swarm intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
SIAM Review
Information Visualization: Perception for Design
Information Visualization: Perception for Design
Visualization of Barrier Tree Sequences
IEEE Transactions on Visualization and Computer Graphics
Topology-Controlled Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
Topological Landscapes: A Terrain Metaphor for Scientific Data
IEEE Transactions on Visualization and Computer Graphics
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
What Hides in Dimension X? A Quest for Visualizing Particle Swarms
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
VISPLORE: a toolkit to explore particle swarms by visual inspection
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Visualizing the search process of particle swarm optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Barrier trees for search analysis
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Bibliometric analysis of particle swarm optimization (PSO) research 2000-2010
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Visualization of High-Dimensional Point Clouds Using Their Density Distribution's Topology
IEEE Transactions on Visualization and Computer Graphics
Large Barrier Trees for Studying Search
IEEE Transactions on Evolutionary Computation
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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.