Enhanced Pareto Particle Swarm Approach for Multi-Objective Optimization of Surface Grinding Process

In the contribution, a new hybrid optimization technique for multi-objective optimization of surface grinding is proposed. The developed approach is based on enhanced Pareto particle swarm optimization algorithm and local climbing optimization technique. Such four process parameters as wheel speed, work piece speed, depth of dressing and lead of dressing are considered as optimization condition and the following three criteria are assumed: production cost, production rate and surface roughness. In order to obtain satisfied Pareto set, an adaptive particle inclusion denseness (PID) variable is introduced as evaluation value to determine the next generation evolution direction for the non-optimal particles. To demonstrate the procedure and performance of the proposed approach, an illustrative example is discussed in detail.