Browse > Article
http://dx.doi.org/10.5762/KAIS.2019.20.6.314

Development of Self-Adaptive Meta-Heuristic Optimization Algorithm: Self-Adaptive Vision Correction Algorithm  

Lee, Eui Hoon (School of Civil Engineering, Chungbuk National University)
Lee, Ho Min (Research Center for Disaster Prevention Science and Technology, Korea University)
Choi, Young Hwan (Research Center for Disaster Prevention Science and Technology, Korea University)
Kim, Joong Hoon (School of Civil, Environmental, and Architectural Engineering, Korea University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.20, no.6, 2019 , pp. 314-321 More about this Journal
Abstract
The Self-Adaptive Vision Correction Algorithm (SAVCA) developed in this study was suggested for improving usability by modifying four parameters (Modulation Transfer Function Rate, Astigmatic Rate, Astigmatic Factor and Compression Factor) except for Division Rate 1 and Division Rate 2 among six parameters in Vision Correction Algorithm (VCA). For verification, SAVCA was applied to two-dimensional mathematical benchmark functions (Six hump camel back / Easton and fenton) and 30-dimensional mathematical benchmark functions (Schwefel / Hyper sphere). It showed superior performance to other algorithms (Harmony Search, Water Cycle Algorithm, VCA, Genetic Algorithms with Floating-point representation, Shuffled Complex Evolution algorithm and Modified Shuffled Complex Evolution). Finally, SAVCA showed the best results in the engineering problem (speed reducer design). SAVCA, which has not been subjected to complicated parameter adjustment procedures, will be applicable in various fields.
Keywords
Self-Adaptive; Vision Correction; Meta-Heuristic; Optimization; Algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Z. W. Geem, "Parameter estimation of the nonlinear Muskingum model using parameter-setting-free harmony search", Journal of Hydrologic Engineering, Vol.16, Issue8, pp.684-688, Aug. 2011. DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0000352   DOI
2 S. Jiang, Y. Zhang, P. Wang, M. Zheng, "An almostparameter-free harmony search algorithm for groundwater pollution source identification", Water Science and Technology, Vol.68, Issue11, pp.2359-2366, Oct. 2013. DOI: https://doi.org/10.2166/wst.2013.499   DOI
3 Y. H. Choi, H. M. Lee, D. G. Yoo, J. H. Kim, "Self-adaptive multi-objective harmony search for optimal design of water distribution networks" Engineering Optimization, Vol.49, Issue11, pp.1957-1977, 2017. DOI: https://doi.org/10.1080/0305215X.2016.1273910   DOI
4 E. H. Lee, D. G. Yoo, Y.H. Choi, J. H. Kim, "Development of the new meta-heuristic optimization algorithm inspired by a vision correction procedure: Vision Correction Algorithm", Journal of the Korea Academia-Industrial cooperation Society, Vol.17, No.3, pp.117-126, March 2016. DOI: https://dx.doi.org/10.5762/KAIS.2016.17.3.117   DOI
5 D. E. Goldberg, J. H. Holland, "Genetic Algorithms and Machine Learning", Machine Learning, Vol.3, Issue2-3, pp.95-99, Oct. 1988. DOI: https://doi.org/10.1023/A:10226020   DOI
6 M. Dorigo, Optimization, learning and natural algorithms, Ph. D. Thesis, Politecnico di Milano, Italy, 1992.
7 K. Deb, H. G. Beyer, "Self-adaptive genetic algorithms with simulated binary crossover", Evolutionary computation, Vol.9, Issue2, pp.197-221, March 2001. DOI: https://doi.org/10.1162/106365601750190406   DOI
8 J. Kennedy, R. Eberhart, "Particle swarm optimization", In Neural Networks, 1995. Proceedings, IEEE International Conference on, IEEE, Perth, Australia Vol.4, pp.1942-1948, Nov. 1995. DOI: https://doi.org/10.1007/978-0-387-30164-8_630
9 Z. W. Geem, J. H. Kim, G. V. Loganathan, "A new heuristic optimization algorithm: harmony search", Simulation, Vol.76, Issue2, pp.60-68, Feb. 2001. DOI: https://doi.org/10.1177/003754970107600201   DOI
10 I. Fister Jr., X.S. Yang, I. Fister, J. Brest, D. Fister, "A Brief Review of Nature-Inspired Algorithms for Optimization", Elektrotehniski vestnik, Vol.80, No.3, pp.1-7, July 2013. DOI: https://arxiv.org/abs/1307.4186
11 M. G. Omran, A. Salman, A. P. Engelbrecht, "Self-adaptive differential evolution", International Conference on Computational and Information Science. Springer, Berlin, Heidelberg, pp.192-199, Dec. 2005. DOI: https://dx.doi.org/10.1007/11596448_28
12 A. Ismail, A. P. Engelbrecht. "Self-adaptive particle swarm optimization" In Asia-Pacific Conference on Simulated Evolution and Learning. Springer, Berlin, Heidelberg, pp.228-237, Dec. 2012. DOI: https://doi.org/10.1007/978-3-642-34859-4_23