Masoudi-Sobhanzadeh Yosef, Motieghader Habib
Department of Computer Engineering, Payam Noor University, Tabriz, Iran.
Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Inform Med Unlocked. 2016;3:15-28. doi: 10.1016/j.imu.2016.06.002. Epub 2016 Jun 28.
Since different sciences face lots of problems which cannot be solved in reasonable time order, we need new methods and algorithms for getting acceptable answers in proper time order. In the present study, a novel intelligent optimization algorithm, known as WCC (World Competitive Contests), has been proposed and applied to find the transcriptional factor binding sites (TFBS) and eight benchmark functions discovery processes. We recognize the need to introduce an intelligent optimization algorithm because the TFBS discovery is a biological and an NP-Hard problem. Although there are some intelligent algorithms for the purpose of solving the above-mentioned problems, an optimization algorithm with good and acceptable performance, which is based on the real parameters, is essential. Like the other optimization algorithms, the proposed algorithm starts with the first population of teams. After teams are put into different groups, they will begin competing against their rival teams. The highly qualified teams will ascend to the elimination stage and will play each other in the next rounds. The other teams will wait for a new season to start. In this paper, we're going to implement our proposed algorithm and compare it with five famous optimization algorithms from the perspective of the following the obtained results, stability, convergence, standard deviation and elapsed time, which are applied to the real and randomly created datasets with different motif sizes. According to our obtained results, in many cases, the WCC׳s performance is better than the other algorithms'.
由于不同学科面临许多无法按合理时间顺序解决的问题，我们需要新的方法和算法，以便能按适当的时间顺序获得可接受的答案。在本研究中，一种名为世界竞争竞赛（WCC）的新型智能优化算法被提出，并应用于寻找转录因子结合位点（TFBS）以及八个基准函数发现过程。我们认识到引入一种智能优化算法的必要性，因为TFBS发现是一个生物学问题且属于NP难问题。尽管有一些智能算法用于解决上述问题，但一种基于实参数且具有良好且可接受性能的优化算法至关重要。与其他优化算法一样，所提出的算法从第一批团队群体开始。团队被分成不同组后，它们将开始与对手团队竞争。高素质的团队将晋级淘汰赛阶段，并在下一轮中相互比赛。其他团队将等待新赛季开始。在本文中，我们将实现我们提出的算法，并从以下方面的结果、稳定性、收敛性、标准差和运行时间的角度，将其与五种著名的优化算法进行比较，这些算法应用于具有不同基序大小的真实和随机创建的数据集。根据我们获得的结果，在许多情况下，WCC的性能优于其他算法。
