• Journal of KIISE:Computer Systems and Theory
• /
• v.32 no.8
• /
• pp.399-410
• /
• 2005

This paper Proposes a novel adaptive genetic algorithm (GA) extrapolated by an ant colony optimization. We first prove that the algorithm converges to the unique global optimal solution with probability arbitrarily close to one and then, by experimental studies, show that the algorithm converges faster to the optimal solution than GA with elitism and the population average fitness value also converges to the optimal fitness value. We further discuss controlling the tradeoff of exploration and exploitation by a parameter associated with the proposed algorithm.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.18 no.9
• /
• pp.36-43
• /
• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.