Advanced SearchSearch Tips
Development of the new meta-heuristic optimization algorithm inspired by a vision correction procedure: Vision Correction Algorithm
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Development of the new meta-heuristic optimization algorithm inspired by a vision correction procedure: Vision Correction Algorithm
Lee, Eui Hoon; Yoo, Do Guen; Choi, Young Hwan; Kim, Joong Hoon;
  PDF(new window)
In this study, a new meta-heuristic optimization algorithm, Vision Correction Algorithm (VCA), designed according to the optical properties of glasses was developed. The VCA is a technique applying optometry and vision correction procedure to optimization algorithm through the process of myopic/hyperopic correction-brightness adjustment-compression enforcement-astigmatism adjustment. The proposed VCA unlike the conventional meta-heuristic algorithm is an automatically adjusting global/local search rate and global search direction based on accumulated optimization results. The proposed algorithm was applied to the representative optimization problem (mathematical and engineering problem) and results of the application are compared with that of the present algorithms.
Vision correction;Optimization;Algorithm;Meta-heuristic;
 Cited by
Application of a meta-heuristic optimization algorithm motivated by a vision correction procedure for civil engineering problems, KSCE Journal of Civil Engineering, 2017, 1976-3808  crossref(new windwow)
D. E. Goldberg and J. H. Holland, "Genetic Algorithms and Machine Learning," Machine Learning, Vol. 3, Issue 2, pp. 95-99, 1988. DOI:

M. Dorigo, Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano, Italy, 1992.

J. Kennedy and R. Eberhart, "Particle swarm optimization," In Neural Networks, 1995. Proceedings, IEEE International Conference on, Vol. 4, pp. 1942-1948, IEEE, 1995. DOI:

Zong Woo Geem, Joong Hoon Kim, and GV Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, Vol. 76, No. 2, pp. 60-68, 2001. DOI: crossref(new window)

I. Fister Jr., X.S. Yang, I. Fister, J. Brest, and D. Fister, “A Brief Review of Nature-Inspired Algorithms for Optimization,” Elektrotehniski vestnik, Vol. 80, No. 3, pp. 1-7, 2013.

D.H. Lim, J.H. Lee, and C.W. Ahn, “Differential Evolution Algorithm using Parallel Processing Structure,” Journal of the Korean Institute of Information Scientists and Engineers, Vol. 37, No. 1, pp. 323-327, 2010.

Y.Y. Chun, H.S. Choi, S.J. Park, and S.J. Lee, “ The Evaluation of Reliability for Exam Distance of Visual Acuity,” Journal of the Korean Ophthalmic Optics Society, Vol. 19, No. 1, pp. 17-22, 2014. DOI: crossref(new window)

H.J. Pahk, S.W. Lee, and W.D. Kim, “Computer Aided Measurement and Compensation System for Focal Length of Lenses in Camera Manufacture Based on the MTF Performance Using the Line CCD Sensor,” Journal of Korean Society for Precision Engineering, Vol. 15, No. 8, pp. 71-80, 1998.

G.S. Che, W.S. Chang, and J. Oh, “A Study on the MTF Graphics using Simpson Approximation,” Journal of the Korea Navigation Institute, Vol. 16, No. 2, pp. 401-408, 2014.

H.J. Bang, J.U. Lee, B.H. Son, K.H. Ahn, and E.J. Choi, “A Study on Assessment of MTF Performance and Theoretical Analysis of Convex Trial Lenses,” Korean Journal of Optics and Photonics, Vol. 24, No. 5, pp. 217-223, 2013. DOI: crossref(new window)

A. Sadollah, H. Eskandar, A. Bahreininejad and J.H. Kim, "Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems," Applied Soft Computing, Vol. 50, May 2015, pp. 58-71, 2015.

A. Sadollah, A. Bahreininejad, H. Eskandar and M. Hamdi, "Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems," Applied Soft Computing, Vol. 13, No. 5, pp. 2592-2612, 2013. DOI: crossref(new window)