Adaptive harmony search with best-based search strategy
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Harmony search (HS) is a new evolutionary algorithm inspired by the process of music improvisation. During the past decade, HS has shown excellent performance in many fields. However, its search strategy often demonstrates insufficient exploitation ability when facing some complex practical problems. Moreover, the HS performance is significantly influenced by its control parameters. To enhance the search efficiency, an adaptive harmony search with best-based search strategy (ABHS) is proposed. In the search process, ABHS exploits the beneficial information from the global-best solution to improve the search ability, while it adaptively tunes its control parameters according to the feedback from the search process. Experiments are conducted on a set of classical test functions. The experimental results show that ABHS significantly enhances the search efficiency of HS.
KeywordsEvolutionary algorithm Harmony search Adaptive Search strategy
This work was supported in part by the National Natural Science Foundation of China (Nos. 61662029, 61462036, and 41561091), the Natural Science Foundation of Jiangxi, China (Nos. 20151BAB217010 and 20151BAB201015), and the Education Department Scientific Research Foundation of Jiangxi Province, China (No. GJJ14433).
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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.
This article does not contain any studies with human participants.
- Gu B, Sheng VS (2016) A robust regularization path algorithm for \(\nu \)-support vector classification. In: IEEE Transactions on Neural Networks and Learning Systems. doi: 10.1109/TNNLS.2016.2527796 (in press)
- Gu B, Sun X, Sheng VS (2016) Structural minimax probability machine. In: IEEE Transactions on Neural Networks and Learning Systems. doi: 10.1109/TNNLS.2016.2544779 (in press)
- Guo Z, Huang H, Deng C, Yue X, Wu Z (2015) An enhanced differential evolution with elite chaotic local search. In: Computational intelligence and neuroscience, Article ID 583759Google Scholar
- Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948Google Scholar
- Papa JP Scheirer W, Cox DD (2015) Fine-tuning deep belief networks using harmony search. Appl Soft Comput 46:875–885Google Scholar
- Shen J, Tan H, Wang J, Wang J, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks. J Internet Technol 16(1):171–178Google Scholar
- Zhao SZ, Suganthan PN, Pan QK, Tasgetiren MF (2011) Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Syst Appl 38(4):3735–3742Google Scholar
- Zheng Y, Jeon B, Xu D, Wu QM, Zhang H (2015b) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar