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Continuous functions minimization by dynamic random search technique

机译：通过动态随机搜索技术最小化连续函数

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摘要

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 1 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.
机译：随机搜索技术是启发式算法中最简单的一种。在文献中指出，通过使用基本随机搜索技术找到全局最小值的概率等于1，但是要花费太多时间才能达到全局最小值。改进基本随机搜索技术可以减少求解时间。在这项研究中，为了快速获得全局最小值，提出了一种新的随机搜索算法。该算法称为动态随机搜索技术（DRASET）。 DRASET由两个阶段组成，分别是常规搜索和基于常规解决方案的本地搜索。保留与在常规搜索过程中找到的最佳解决方案相关的知识，然后将该知识用作本地搜索的初始值。通过15个测试问题对DRASET的性能进行了实验，并获得了满意的结果。

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