Technique for Multi-Pass Turning Optimization Based on Gaussian Quantum-Behaved Bat Algorithm

The multi-pass turning operation is one of the most commonly used machining methods in manufacturing field. The main objective of this operation is to minimize the unit production cost. This paper proposes a Gaussian quantum-behaved bat algorithm (GQBA) to solve the problem of multi-pass turning operation. The proposed algorithm mainly includes the following two improvements. The first improvement is to incorporate the current optimal positions of quantum bats and the global best position into the stochastic attractor to facilitate population diversification. The second improvement is to use a Gaussian distribution instead of the uniform distribution to update the positions of the quantum-behaved bats, thus performing a more accurate search and avoiding premature convergence. The performance of the presented GQBA is demonstrated through numerical benchmark functions and a multi-pass turning operation problem. Thirteen classical benchmark functions are utilized in the comparison experiments, and the experimental results for accuracy and convergence speed demonstrate that, in most cases, the GQBA can provide a better search capability than other algorithms. Furthermore, GQBA is applied to an optimization problem for multi-pass turning, which is designed to minimize the production cost while considering many practical machining constraints in the machining process. The experimental results indicate that the GQBA outperforms other comparison algorithms in terms of cost reduction, which proves the effectiveness of the GQBA.