Hybrid algorithms based on harmony search and differential evolution for global optimization

ABSTRACT

In this paper, two hybrid algorithms are proposed for global optimization by merging the mechanisms of Harmony Search (HS) and Differential Evolution (DE). First, the learning mechanism of a variant of HS named Global-best Harmony Search (GHS) is embedded into the framework of DE to develop an algorithm called Global Harmony Differential Evolution (GHDE). Besides, the differential operator of DE is introduced into the framework of GHS to develop another new algorithm called Differential Harmony Search (DHS). Numerical simulations are carried out based a set of benchmarks. And simulation results and comparisons show that the hybrid algorithms are superior to the GHS and DE in terms of searching efficiency and searching quality. Meanwhile, the effect of some key parameters on the performances of DHS is investigated.

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INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Global optimization

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Unconstrained optimization; Global optimization

General Terms:
Algorithms

Keywords:
differential evolutionary, global optimization, global-best harmony searching, hybrid algorithms