Driving Training-Based Optimization: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Abstract In this paper, a new stochastic optimization algorithm, called Driving Training-Based Optimization (DTBO), is introduced, which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the process of learning to drive at driving school and driving instructor training. DTBO is mathematically modeled in three phases: (i) training by the driving instructor, (ii) patterning of students from instructor skills, and (iii) practice. The performance of DTBO in optimization is evaluated on a set of twenty-three standard objective functions of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal 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 ten well-known algorithms. The simulation results show that DTBO has better performance compared to ten competitor algorithms and is more efficient in optimization applications.