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

Area of Science:

• Engineering
• Optimization Algorithms
• Mining Technology

Background:

• Tunneling equipment development, particularly roadheaders, lags behind, hindering efficient coal mine production.
• Roadheader shovel plate parameters significantly impact performance, necessitating optimization for improved reliability and design.
• Conventional multi-objective optimization methods have limitations, including reliance on prior knowledge and susceptibility to initialization issues.

Purpose of the Study:

• To develop and validate an improved particle swarm optimization (PSO) algorithm for multi-objective roadheader shovel plate parameter optimization.
• To enhance roadheader performance by optimizing shovel plate parameters for reduced resistance and increased load capacity.
• To provide a practical and efficient multi-objective optimization method for engineering applications.

Main Methods:

• An improved particle swarm optimization (PSO) algorithm was developed, using minimum Euclidean distance for evaluating extreme values.
• The algorithm facilitates multi-objective parallel optimization, generating a non-inferior solution set.
• Grey decision theory was applied to select the optimal solution from the non-inferior set.

Main Results:

• The optimized roadheader shovel plate parameters are a width (l) of 3.144 m and an inclination angle (β) of 16.88°.
• Optimization resulted in a 14.3% decrease in shovel plate mass.
• Propulsive resistance decreased by 6.62%, while load capacity increased by 3.68%.

Conclusions:

• The proposed multi-objective optimization method, combining improved PSO and grey decision, is feasible and effective.
• Optimized shovel plate parameters significantly improve roadheader performance by reducing resistance and increasing load capacity.
• This method offers a convenient approach for multi-objective optimization in practical engineering scenarios.