Visual Analysis of Discrete Particle Swarm Optimization Using Fitness Landscapes

  • Sebastian Volke
  • Simon Bin
  • Dirk Zeckzer
  • Martin Middendorf
  • Gerik Scheuermann
Part of the Emergence, Complexity and Computation book series (ECC, volume 6)


Particle swarm optimization (PSO) is a metaheuristic where a swarm of particles moves within a search space in order to find an optimal solution. PSO has been applied to continuous and combinatorial optimization problems in various application areas. As is typical for metaheuristics, it is also not easy for PSO for algorithm designers to understand in detail how and why changes in the design of a PSO algorithm influence its optimization behavior. It is shown in this chapter that a suitable visualization of the optimization process can be very helpful for understanding the optimization behavior of PSO algorithms. In particular, it is explained how the visualization tool dPSO-Vis can be used to analyze the optimization behavior of PSO algorithms. The two example PSO algorithms that are used are the SetPSO and the HelixPSO. Both algorithms can be used for solving the RNA secondary structure prediction problem.


Particle Swarm Optimization Particle Swarm Optimization Algorithm Global Memory Fitness Landscape Optimization Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sebastian Volke
    • 1
  • Simon Bin
    • 1
  • Dirk Zeckzer
    • 2
  • Martin Middendorf
    • 1
  • Gerik Scheuermann
    • 1
  1. 1.Department of Computer ScienceUniversity of LeipzigLeipzigGermany
  2. 2.Department of Computer ScienceUniversity of KaiserslauternKaiserslauternGermany

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