Clustering Search and Variable Mesh Algorithms for continuous optimization

Yasel Josè Costa Salas

Research output: Contribution to journalArticle

4 Citations (Scopus)


The hybridization of population-based meta-heuristics and local search strategies is an effective algorithmic proposal for solving complex continuous optimization problems. Such hybridization becomes much more effective when the local search heuristics are applied in the most promising areas of the solution space. This paper presents a hybrid method based on Clustering Search (CS) to solve continuous optimization problems. The CS divides the search space in clusters, which are composed of solutions generated by a population meta-heuristic, called Variable Mesh Optimization. Each cluster is explored further with local search procedures. Computational results considering a benchmark of multimodal continuous functions are presented.

Original languageEnglish
Pages (from-to)789-795
Number of pages7
JournalExpert Systems with Applications
Issue number2
Publication statusPublished - 1 Feb 2015


  • Continuous function optimization
  • Hybrid methods


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