Optimization of open-pit mine logistics system based on improved artificial fish swarm algorithm

In order to improve the transportation efficiency and product quality of open-pit mine logistics systems, and reduce transportation costs, an optimization method for open-pit mine logistics systems based on improved artificial fish swarm algorithm is studied. Construct an optimization model for open-pit mine logistics system with the objective functions of minimizing ore grade deviation, minimizing transportation costs, and minimizing total waiting time, combined with constraints such as production capacity, ore output, and ore grade of the mining site. Using the artificial fish swarm algorithm for model solving, the artificial fish is considered as a candidate solution for transportation schemes. By simulating foraging, random swimming, clustering, and rear end collision behaviors, the optimal transportation scheme for the open-pit mining logistics system is found. At the same time, the improved artificial fish swarm algorithm is introduced through dynamic field of view setting, crowding degree setting, reverse learning mechanism, and Cauchy mutation to efficiently search for the global optimal scheduling scheme. The experimental results show that the application of this method can achieve a reduction in transportation costs, a decrease in grade deviation of molybdenum and tungsten, and a decrease in total waiting time for transportation, all while meeting the target output demand for ore. Moreover, the solution space distribution obtained by improving the artificial fish swarm algorithm is more dispersed and closer to the global optimal boundary, which is more advantageous in multi-objective balance and global optimization.