Abstract
he possibilities of improving sorting time parameters through preprocessing by stochastic sorting were investigated. The hypothesis that two-component stochastic + classical sorting outperforms classic one-component sorting in terms of time efficiency was experimentally confirmed. Sorting with different computational complexity is accepted as classical sorting algorithms: shaker sort- ing with computational complexity O(n2), insertions O(n2), Shell O(n·(log n)2) ... O(n3/2), fast with optimization of ending sequences O(n·log n). The greatest effect is obtained when performing comparisons using stochastic sorting in the amount of 160 percent of the array’s size. Indicators of the efficiency of the exchange of two elements, as well as series of exchanges, are introduced. This allowed to determine the highest efficiency of stochastic sorting (as the first component of two-component sorting), when one element for comparison is chosen from the first part of the array and the other from the second. For algorithms with a computational complexity of O(n2) the improvement in time efficiency reached 70–80 percent. However, for Shell sort and quick sort, the stochas- tic presort has no positive effect, but instead increases the total sorting time, which is apparently due to the initial high efficiency of these sorting methods. The hypothesis that three-component sorting fast + stochastic + insertions would increase sorting time efficiency was not confirmed. However, during the experiment, the recommended size of the array was determined, at which point the two-component quick + insertion sort must be switched to the second component – insertion sorting. The optimal length of the ending sequences is between 60 and 80 elements. Given that algorithm time efficiency is affected by computer architecture, operat- ing system, software development and execution environment, data types, data sizes, and their values, time efficiency indicators should be specified in each specific case.
Publisher
National Academy of Sciences of Ukraine (Co. LTD Ukrinformnauka) (Publications)
Reference18 articles.
1. 1. SHYNKARENKO, V., ILCHENKO, P. & ZABULA, H. (2018) Tools of investigation of time and functional efficiency of bionic algo- rithms for function optimization problems. In International Conference of Programming. Kyiv, Tuesday 22th to Thursday 24th May 2018. CEUR Workshop Proceedings. 2139. p. 270-280.
2. 2. CORMEN, T. H. et al. (2009) Introduction To Algorithms (3rd ed.). Cambridge, MA: The MIT Press, 1292 p.
3. Comparative study of various stable and unstable sorting algorithms;YADAV;Artificial Intel- ligence and Speech Technology,2021
4. 4. WIKIPEDIA. (2022) Sorting algorithm. [Online] July 2022. Available from: URL: https://en.wikipedia.org/wiki/Sorting_algorithm [Accessed: 11th July 2022].
5. 5. KNUTH, D. (1973) The Art of Programming, Vol. 3 Sorting and Searching.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献