An improved differential harmony search algorithm for function optimization problems
- 50 Downloads
To overcome the drawbacks of the harmony search (HS) algorithm and further enhance its effectiveness and efficiency, an improved differential HS (IDHS) is proposed to solve numerical function optimization problems. The proposed IDHS has a novel improvisation scheme that integrates DE/best/1/bin and DE/rand/1/bin from the differential evolution (DE) algorithm to enhance its local search and exploration capabilities and a new pitch adjustment rule that benefits from the best solution in the harmony memory to increase its convergence speed. With dynamically adjusted parameters, the proposed IDHS can balance exploitation and exploration throughout the search process. The numerical results of an experiment with classic testing functions and those of a comparative experiment show that IDHS outperforms eight algorithms in the HS family and three widely used population-based algorithms in different families, including DE, particle swarm optimization, and improved fruit fly optimization algorithm. IDHS demonstrates fast convergence and an especially good capability to handle difficult high-dimensional optimization problems.
KeywordsHarmony search algorithm Differential evolution Meta-heuristics Global optimization
This research is partially supported by National Natural Science Foundation of China (71771096; 71531009).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Geem ZW (2011) Discussion on “Combined heat and power economic dispatch by harmony search algorithm” by A. Vasebi et al. Int J Electr Power Energy Syst 29(2007):713–719. Int J Electr Power Energy Syst 33(7):1348–1348Google Scholar
- Pan QK, Suganthan PN, Tasgetiren MF, Liang JJ (2010) A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl Math Comput 216(3):830–848Google Scholar
- Park J, Kwon S, Kim M, Han S (2017) A cascaded improved harmony search for line impedance estimation in a radial power system. IFAC-PapersOnLine 50(1):3368–3375Google Scholar
- Shen Q, Jiang JH, Jiao CX, Shen GL, Yu RQ (2004) Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists. Eur J Pharm Sci 22(2):145–152Google Scholar
- Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359Google Scholar
- Villarrubia G, De Paz JF, Chamoso P, De la Prieta F (2018) Artificial neural networks used in optimization problems. Neurocomputing 272:10–16Google Scholar
- Wang G, Guo L (2013) A novel hybrid bat algorithm with harmony search for global numerical optimization. J Appl Math Article ID 696491, 1–21Google Scholar
- Wang CM, Huang YF (2010) Self-adaptive harmony search algorithm for optimization. Expert Syst Appl 37(4):2826–2837Google Scholar
- Wang L, Liu R, Liu S (2016) An effective and efficient fruit fly optimization algorithm with level probability policy and its applications. Knowl-Based Syst 97:158–174Google Scholar