A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process.
A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process.
复制标题
DOI：
10.1038/s41598-022-14225-7
复制
发表时间：
2022-06-15
影响因子：
4.6
通讯作者：
Trojovsky, Pavel
中科院分区：
文献类型：
--
作者：
Dehghani, Mohammad;Trojovska, Eva;Trojovsky, Pavel
本文介绍了一种新的随机优化算法，称为驾驶训练为基础的优化（DTBO），它模仿人类的驾驶训练活动。DTBO设计背后的基本灵感是在驾驶学校学习驾驶的过程和驾驶教练的培训。DTBO的数学模型分为三个阶段：（1）驾驶教练的训练，（2）教练技能的学生模式，（3）实践。DTBO在优化中的性能在一组53个标准目标函数上进行评估，这些目标函数包括单峰、高维多峰、固定维多峰和IEEE CEC2017测试函数类型。优化结果表明，DTBO能够通过保持勘探和开采之间的适当平衡，为优化问题提供适当的解决方案。DTBO的性能质量进行了比较与11个著名的算法的结果。仿真结果表明，DTBO算法的性能优于11个竞争对手的算法，是更有效的优化应用。
In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications.
登录
查看更多内容
影响因子：
7
作者：
Abdollahzadeh, Benyamin;Gharehchopogh, Farhad Soleimanian;Mirjalili, Seyedali
通讯作者：
Mirjalili, Seyedali
影响因子：
6.6
作者：
IBA, K
通讯作者：
IBA, K
影响因子：
2.7
作者：
Dehghani, Mohammad;Montazeri, Zeinab;Parra-Arroyo, Lizeth
通讯作者：
Parra-Arroyo, Lizeth
影响因子：
8.8
作者：
Faramarzi, Afshin;Heidarinejad, Mohammad;Mirjalili, Seyedali
通讯作者：
Mirjalili, Seyedali
影响因子：
8.5
作者：
Jiang, Yuxin;Wu, Qing;Zhang, Luke
通讯作者：
Zhang, Luke