TY - JOUR
T1 - Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing
AU - Wang, Z. G.
AU - Rahman, M.
AU - Wong, Y. S.
AU - Sun, J.
PY - 2005/12
Y1 - 2005/12
N2 - This paper presents an approach to select the optimal machining parameters for multi-pass milling. It is based on two recent approaches, genetic algorithm (GA) and simulated annealing (SA), which have been applied to many difficult combinatorial optimization problems with certain strengths and weaknesses. In this paper, a hybrid of GA and SA (GSA) is presented to use the strengths of GA and SA and overcome their weaknesses. In order to improve, the performance of GSA further, the parallel genetic simulated annealing (PGSA) has been developed and used to optimize the cutting parameters for multi-pass milling process. For comparison, conventional parallel GA (PGA) is also chosen as another optimization method. An application example that has been solved previously using the geometric programming (GP) and dynamic programming (DP) method is presented. From the given results, PGSA is shown to be more suitable and efficient for optimizing the cutting parameters for milling operation than GP+DP and PGA.
AB - This paper presents an approach to select the optimal machining parameters for multi-pass milling. It is based on two recent approaches, genetic algorithm (GA) and simulated annealing (SA), which have been applied to many difficult combinatorial optimization problems with certain strengths and weaknesses. In this paper, a hybrid of GA and SA (GSA) is presented to use the strengths of GA and SA and overcome their weaknesses. In order to improve, the performance of GSA further, the parallel genetic simulated annealing (PGSA) has been developed and used to optimize the cutting parameters for multi-pass milling process. For comparison, conventional parallel GA (PGA) is also chosen as another optimization method. An application example that has been solved previously using the geometric programming (GP) and dynamic programming (DP) method is presented. From the given results, PGSA is shown to be more suitable and efficient for optimizing the cutting parameters for milling operation than GP+DP and PGA.
KW - Genetic algorithm
KW - Milling
KW - Parallel genetic algorithm
KW - Simulated annealing
UR - http://www.scopus.com/inward/record.url?scp=25144483853&partnerID=8YFLogxK
U2 - 10.1016/j.ijmachtools.2005.03.009
DO - 10.1016/j.ijmachtools.2005.03.009
M3 - Article
AN - SCOPUS:25144483853
SN - 0890-6955
VL - 45
SP - 1726
EP - 1734
JO - International Journal of Machine Tools and Manufacture
JF - International Journal of Machine Tools and Manufacture
IS - 15
ER -