Parameters optimization of a multi-pass milling process based on imperialist competitive algorithm

In multi-pass milling, the selection of machining parameters is of great significance since the parameters affect the production time, quality, cost, and some other process performance measures greatly. However, the parameters optimization of the multi-pass milling process is a nonlinear constrained optimization problem. It is very difficult to obtain the satisfactory solutions by the traditional optimization methods. Therefore, in this paper, a new optimization technique based on imperialist competitive algorithm (ICA) is proposed to solve the parameters optimization problem in multi-pass milling process. The ICA is a population based meta-heuristic algorithm for unconstrained optimization problems. To address the constraints efficiently, the proposed approach introduces two constraints handling techniques, which include the penalty function method and the constraints handling strategy of ICA. A case study is presented to verify the effectiveness of the proposed method. The results show that the proposed method is better than other algorithms and achieves significant improvement.