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Soft Computing

, Volume 16, Issue 3, pp 511–525 | Cite as

Variable mesh optimization for continuous optimization problems

  • Amilkar Puris
  • Rafael Bello
  • Daniel Molina
  • Francisco Herrera
Original Paper

Abstract

Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic called “variable mesh optimization” (VMO), in which the set of nodes (potential solutions) are distributed as a mesh. This mesh is variable, because it evolves to maintain a controlled diversity (avoiding solutions too close to each other) and to guide it to the best solutions (by a mechanism of resampling from current nodes to its best neighbour). This proposal is compared with basic population-based meta-heuristics using a benchmark of multimodal continuous functions, showing that VMO is a competitive algorithm.

Keywords

Particle Swarm Optimization Search Space Local Extreme Node Generation Mesh Node 
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 2011

Authors and Affiliations

  • Amilkar Puris
    • 1
  • Rafael Bello
    • 1
  • Daniel Molina
    • 2
  • Francisco Herrera
    • 3
  1. 1.Department of Computer ScienceUniversidad Central de las VillasSanta ClaraCuba
  2. 2.Department of Computer Languages and SystemsUniversity of CadizCadizSpain
  3. 3.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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