Continuous functions minimization by dynamic random search technique

Hamzacebi C., Kutay F.

APPLIED MATHEMATICAL MODELLING, vol.31, no.10, pp.2189-2198, 2007 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 10
  • Publication Date: 2007
  • Doi Number: 10.1016/j.apm.2006.08.015
  • Page Numbers: pp.2189-2198
  • Keywords: random search, global search, local search, global minimum, random optimization, OPTIMIZATION, ALGORITHM


Random search technique is the simplest one of the heuristic algorithms. It is stated in the literature that the probability of finding global minimum is equal to I by using the basic random search technique, but it takes too much time to reach the global minimum. Improving the basic random search technique may decrease the solution time. In this study, in order to obtain the global minimum fastly, a new random search algorithm is suggested. This algorithm is called as the Dynamic Random Search Technique (DRASET). DRASET consists of two phases, which are general search and local search based on general solution. Knowledge related to the best solution found in the process of general search is kept and then that knowledge is used as initial value of local search. DRASET's performance was experimented with 15 test problems and satisfactory results were obtained. (c) 2006 Elsevier Inc. All rights reserved.