The hybrid strategies of harmony search in optimization problem solving

Xiaolei Wang*, Xiao Zhi Gao, Kai Zenger

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

1 Citation (Scopus)


The remarkable capability of overcoming the shortcomings of NIC individual algorithms without losing their advantages makes the hybrid NIC techniques superior to the stand-alone ones and become a dominant research topic in the field of NIC methods. This hybridization mechanism has been demonstrated to achieve great improvement in the performance of different optimization issues. In this chapter, four hybrid optimization methods based on the HS combined with niching technique, opposition-based learning (OBL), clonal selection algorithm, and cultural algorithm are presented. The simulation results show that they can considerably outperform the original HS method in the applications of multimodal optimization, optimization of high-dimensional benchmark functions, minimization of weight of a tension/compression spring, optimal Sugeno fuzzy classification systems, and optimal design of welded beam, gear train, pressure vessel, and permanent magnet direct-driven wind generator.

Original languageEnglish
Title of host publicationSpringerBriefs in Applied Sciences and Technology
Number of pages53
Publication statusPublished - 2015
MoE publication typeA3 Book section, Chapters in research books

Publication series

NameSpringerBriefs in Applied Sciences and Technology
ISSN (Print)2191-530X
ISSN (Electronic)2191-5318


  • Cultural algorithm
  • Harmony search (HS) method
  • Hybrid harmony search (HS) methods
  • Multimodal optimization
  • Niching
  • Opposition-based learning
  • Wind generator design


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