Abstract
In this paper, the improvement of pure random search is studied. By taking some information of the function to be minimized into consideration, the authors propose two stochastic global optimization algorithms. Some numerical experiments for the new stochastic global optimization algorithms are presented for a class of test problems.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Brooks S. H., A discussion of random methods for seeking maxima, Operations Research, 1958, 6:244–251
Anderssen R.S., Global optimization, In R. S. Anderssen, L.S. Jennings and D. M. Ryan (Editors), Optimization, University of Queensland Press, 1972
Dixon L. C. W. and Szegö G. P., eds. Towards Global Optimization 2, North-Holland, Amsterdam, 1978
Dixon L. C. W. and Szegö G. P., eds. Towards Global Optimization 1, North-Holland, Amsterdam, 1978
Rinnooy Kan A. G. H. and Timmer G. T., Stochastic methods for global optimization. American Journal of Mathematical and Management Sciences, 1984,4:7–40
Devroye L., Progressive global random search of continuous functions, Mathematical programming, 1978,15:330–342
Rubinstein R. Y., Simulation and Monte Carlo Method, John Wiley and Sons, New York, 1981
Zabinsky Z. B. and Smith R. L., Pure adaptive search in global optimization, Mathematical Programming, 1992, 53:323–338
Price W. L., A controlled random search procedure for global optimization, in: Dixon and Szegö, eds. Towards Global Optimization 2, North-Holland, Amsterdam, 1978
Price W. L., Global optimization by controlled random search, JOTA, 1983, 40:333–348
Price W. L., Global optimization algorithms for CAD workstation, JOTA, 1987, 55:133–146
Nelder J. A. and Mead R., A simplex method for function minimization, Computer Journal, 1965, 7:308–313
Mohan C. and Kuanker Shanker, A numerical study of modified versions of controlled random search method for global optimization, Intern. J. Computer Math., 1988, 23:325–341
Romeijn H. E., Global Optimization by Random Walk Sampling Methods, PhD. Thesis, Erasmus University Rotterdam, 1992
Kirkpatrick S., Gelatt C. D. Jr., Vecchi M. P., Optimization by simulated annealing, Science, 1983,20: 671–680
Timmer G. T., Global Optimization: a Stochastic Approach, Ph D. Thesis, Erasmus University Rotterdam, Rotterdam, The Netherlands, 1984
Bremmerman H., A method of unconstrained global optimization, Mathematical Biosciences, 1970,9:1–15
Aluffi-Pentini F., Parisi V. and Zirilli F., Global optimization and stochastic differential equations, Journal of Optimization Theory and Applications, 1985, 47:1–16
Author information
Authors and Affiliations
Additional information
Supported by the Science Foundation of Shanghai Municipal Commission of Education
About this article
Cite this article
Peng, Jp., Shi, Dh. Improvement of pure random search in global optimization. J. of Shanghai Univ. 4, 92–95 (2000). https://doi.org/10.1007/s11741-000-0002-4
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/s11741-000-0002-4