Fine-Tuning Meta-Heuristic Algorithm for Global Optimization
Fine-Tuning Meta-Heuristic Algorithm for Global Optimization
复制标题
DOI：
10.3390/pr7100657
复制
发表时间：
2019-10-01
期刊：
影响因子：
3.5
通讯作者：
Humaidi, Amjad J.
中科院分区：
文献类型：
--
作者：
Allawi, Ziyad T.;Ibraheem, Ibraheem Kasim;Humaidi, Amjad J.
本文提出了一种新颖的元启发式优化算法，称为微调元启发式算法（FTMA），用于解决全局优化问题。在该算法中，使用元启发式优化的基本步骤（即探索、利用和随机化）对解决方案进行微调，这样如果一个步骤改进了解决方案，则无需执行其余步骤。所提出的 FTMA 的性能已与十多个基准测试函数中的其他五种优化算法的性能进行了比较。其中九个是众所周知的并且已经存在于文献中，而第十个是由作者提出并在本文中介绍的。一项测试是为了检查每种算法的性能，另一项测试是为了衡量所提出的算法相对于其他算法的性能的统计结果，进行了 30 次试验。结果证实，所提出的 FTMA 全局优化算法在速度和避免局部最小值方面与同类算法相比具有竞争性能。
This paper proposes a novel meta-heuristic optimization algorithm called the fine-tuning meta-heuristic algorithm (FTMA) for solving global optimization problems. In this algorithm, the solutions are fine-tuned using the fundamental steps in meta-heuristic optimization, namely, exploration, exploitation, and randomization, in such a way that if one step improves the solution, then it is unnecessary to execute the remaining steps. The performance of the proposed FTMA has been compared with that of five other optimization algorithms over ten benchmark test functions. Nine of them are well-known and already exist in the literature, while the tenth one is proposed by the authors and introduced in this article. One test trial was shown to check the performance of each algorithm, and the other test for 30 trials to measure the statistical results of the performance of the proposed algorithm against the others. Results confirm that the proposed FTMA global optimization algorithm has a competing performance in comparison with its counterparts in terms of speed and evading the local minima.