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


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.


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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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