基于改进蜂群算法的井下防爆柴油车多目标优化

Multi-objective optimization of underground explosion-proof diesel vehicle based on improved bee colony algorithm

为了保障井下煤矿员工健康,响应碳中和号召,提出一种改进后的人工蜂群算法(modify artificial bee colony,Modify-ABC)来降低油耗、减少尾气排放。以5 t和8 t的井下防爆材料车为研究对象,建立防爆柴油机数学模型,提出降低有害气体排放及油耗的多目标优化模型,用Modify-ABC与其他3种算法进行对比,对目标函数进行优化求解。结果表明,Modify-ABC相比于传统蜂群算法,遗传算法和模拟退火算法收敛速度更快更好,使用改进算法后,井下5 t材料车油耗及有害气体排放量减少了25%,8 t材料车油耗及有害气体排放量减少了24.8%。

In order to protect the health of underground coal mine employees and respond to the call of carbon neutrality,an modify artificial bee colony(Modify-ABC) algorithm was proposed to reduce fuel consumption and exhaust emissions.The mathematical model of explosion-proof diesel engine is established on 5 t and 8 t explosion-proof material trucks,as the goal,a multi-objective optimization model was proposed to reduce harmful gas emissions and fuel consumption.The Modify-ABC was used to compare with the other three algorithm,and to optimize the objective function.The results show that the rate of convergence of the Modify-ABC is better than those of the traditional bee colony algorithm,genetic algorithm and simulate anneal algorithm.After using the improved algorithm,the fuel consumption and harmful gas emission of 5 t material truck are reduced by 25%,and that of 8 t material truck is reduced by 24.8%.