cuckoo optimization algorithm in cutting conditions during machining

optimization of cutting conditions is a non-linear optimization with constraint and it is very important to the increase of productivity and the reduction of costs. in recent years, several evolutionary and meta-heuristic optimization algorithms were introduced. the cuckoo optimization algorithm (coa) is one of several recent and powerful meta-heuristics which is inspired by the cuckoos and their lifestyle. in this paper, coa, simulated annealing (sa), genetic algorithm (ga) and imperialist competitive algorithm (ica) are first applied to five test functions and the performance of these algorithms is compared. these algorithms are then used to optimize the cutting conditions. the results showed that coa has more capabilities such as accuracy, faster convergence and better global optimum achievement than others.