1.School of Resource Engineering, Xi’an University of Architecture and Technology,Xi'2.'3.an Key Laboratory of Intelligent Industry Perception Computing and Decision Making;4.School of management,Xi’an University of Architecture and Technology,Xi'
General supported project of National Natural Science Foundation of China: Research on unmanned multi process and multi-objective collaborative intelligent scheduling method of metal open pit mine (52074205); Project supported by outstanding youth of Natural Science Foundation of Shaanxi Province: Integrated Modeling of driverless multi vehicle Cooperative Intelligent Dispatching of metal open pit mine under space-time road conditions (2020jc-44)
Aiming at the disadvantages of slow convergence, low optimization accuracy and easy to fall into local extremum in sparrow search algorithm for solving large-scale optimization problems, a firefly sparrow search algorithm based on elite reverse learning strategy (elfassa) is proposed. Firstly, the population is initialized by reverse learning strategy to lay the foundation for global optimization; Secondly, the firefly perturbation strategy is used to improve the ability of the algorithm to jump out of the local optimum and accelerate the convergence; Finally, after the sparrow position is updated, the elite reverse learning strategy is introduced to obtain the elite solution and dynamic boundary, so that the elite reverse solution can be located in the narrow search space, which is conducive to the convergence of the algorithm. By selecting 10 high-dimensional standard test functions for simulation experiments, its performance is compared with sparrow search algorithm (SSA) and four advanced improved algorithms, and the effectiveness of the improved strategy is analyzed with three single strategy improved sparrow search algorithms. The simulation results show that elfassa algorithm is obviously superior to other comparison algorithms in convergence speed and solution accuracy.