1. Key Laboratory of Industrial Computer Control Engineering of Hebei Province,Yanshan University,Qinhuangdao 066004,Hebei,China 2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,Yanshan University,Qinhuangdao 066004,Hebei,China)
Abstract:Rolling schedule is set by the model of optimize first and then choice to solve the difficult problem of select weight,because there is coupling and restrict each other between objective functions. In order to improve the efficiency of mill,improve the plate shape and surface quality,selecting the power distribution,excellent flatness and the slip rate as objective functions,The rolling schedule of steel cold rolling mill in Tangshan is optimized by Improve Immune Clone Multi- objective Algorithm(IICMA). The experiment results show that IICMA can guarantee convergence and improve the distribution degree of the population at the same time;and the optimized different preferences rolling schedules can meet the requirements of different choices. Compared with the original procedures,the use of the mill is more reasonable,flatness is better and the slip rate reduces to an acceptable level.
Coello C A C, Cortes N C.Solving multi-objective optimization problems using an artificial immune system[J].Genetic Programming and Evolvable Machines, 2005, 6(2):163-190
[3]
Coello C A C, Cortes N C.Solving multi-objective optimization problems using an artificial immune system[J].Genetic Programming and Evolvable Machines, 2005, 6(2):163-190
Li M Q, Zheng J H, Shen R M, Li K, Yuan Q Z.A Grid-Based Fitness Strategy for Evolutionary Many-Objective Optimization[J].In: Proceedings of Genetic and Evolutionary Computation Conference, 2010, 1(1):463-470
[8]
Li M Q, Zheng J H, Shen R M, Li K, Yuan Q Z.A Grid-Based Fitness Strategy for Evolutionary Many-Objective Optimization[J].In: Proceedings of Genetic and Evolutionary Computation Conference, 2010, 1(1):463-470
[9]
Li M Q, Zheng J H, Li K, Yuan Q Z, Shen R M.Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization[J].In: Proceedings of Parallel Problem Solving from Nature - PPSN XI, 11th International Conference, 2010, 1(1):647-656
[9]
Li M Q, Zheng J H, Li K, Yuan Q Z, Shen R M.Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization[J].In: Proceedings of Parallel Problem Solving from Nature - PPSN XI, 11th International Conference, 2010, 1(1):647-656
[10]
M.Laumanns,LThiele,K.Deb,Eckart Zitzler.Combining convergence and diversity in evolutionary multi-objective optimization[J].Evolutionary Computation, 2002, 10(3):263-282
[10]
M.Laumanns,LThiele,K.Deb,Eckart Zitzler.Combining convergence and diversity in evolutionary multi-objective optimization[J].Evolutionary Computation, 2002, 10(3):263-282
[11]
Helbig S, Pateva D.On several concepts for ?-efficiency[J].Operations-Research-Spektrum, 1994, 16(3):179-186
[11]
Helbig S, Pateva D.On several concepts for ?-efficiency[J].Operations-Research-Spektrum, 1994, 16(3):179-186
[12]
Van Veldhuizen David A and Gary B. Lamont.. Evolutionary Computation and Convergence to a Pareto Front[J].Late Breaking Papers at the Genetic Programming 1998 Conference, 1988, 1(1):221-228
[12]
Van Veldhuizen David A and Gary B. Lamont.. Evolutionary Computation and Convergence to a Pareto Front[J].Late Breaking Papers at the Genetic Programming 1998 Conference, 1988, 1(1):221-228
[13]
Schott Jason R.Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization[J].AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH, 1995, 1(1):76-136
[13]
Schott Jason R.Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization[J].AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH, 1995, 1(1):76-136