Multiobjective optimization of roadheader shovel-plate parameters based on improved particle swarm optimization and grey decision.

Abstract

The development of tunneling equipment still lags behind, limiting rapid and accurate tunneling and restricting efficient production in coal mines. Thus, improving the reliability and design of roadheaders becomes essential. As the shovel plate is an essential part of a roadheader, improving its parameters can increase the roadheader performance. The parameter optimization of roadheader shovel plate is multi-objective optimization. Because of conventional multiobjective optimization requires strong prior knowledge, often provides low-quality results, and presents vulnerability to initialization and other shortcomings when used in practice. We propose an improved particle swarm optimization (PSO) algorithm that takes the minimum Euclidean distance from a base value as the evaluation criterion for global and individual extreme values. The improved algorithm enables multiobjective parallel optimization by providing a non-inferior solution set. Then, the optimal solution is searched in this set using grey decision to obtain the optimal solution. To validate the proposed method, the multiobjective optimization problem of the shovel-plate parameters is formulated for its solution. Before optimization shovel-plate most important parameters l is the width of the shovel plate = 3.2 m, is the inclination angle of the shovel plate and β = ,19°. When doing optimization, set accelerated factor , population size N = 20, and maximum number of iterations = 100. In addition, speed V was restricted by , and inertia factor W was dynamic and linearly decreasing, , with and . In addition, and were set randomly in [0, 1], while optimization degree η was set to 30%. And then we executed the improved PSO, obtaining 2000 non-inferior solutions. Apply grey decision to find the optimal solution. The optimal roadheader shovel-plate parameters are l = 3.144 m and β = 16.88°. Comparative analysis is made before and after optimization, the optimized parameters were substituted into the model and simulated. Obtained that the optimized parameters of shovel-plate can reduce the mass of the shovel plate decreases by 14.3%, while the propulsive resistance decreases by 6.62%, and the load capacity increases by 3.68%. Thus jointly achieving the optimization goals of reducing the propulsive resistance while increasing the load capacity. The feasibility of the proposed multiobjective optimization method with improved particle swarm optimization and grey decision is verified, and the method can provide convenient multiobjective optimization in engineering practice.