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

Authors:
Ling-po Li;Ling Wang
Affiliations:
Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China;Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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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.