多目标炼钢—连铸生产调度的改进带精英策略的快速非支配排序遗传算法
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摘要
针对炼钢连铸调度的特殊工艺要求,在考虑炉机匹配原则和多重精炼的情况下,建立了以炉机匹配度、炉次间等待时间、浇次的开浇提前/拖期时间为评价指标的多目标约束满足优化模型,并针对其多目标特征,提出一种基于自适应网格法的择优策略来改进带精英策略的快速非支配排序遗传算法,有效克服了使用传统Pareto支配法择优策略在解决离散问题时容易丢失有用信息的缺陷。基于多种规模的实际生产数据进行仿真实验,结果表明所提算法在收敛性、最优解集多样性和计算效率方面优于传统带精英策略的快速非支配排序遗传算法。
Aiming at the special process requirements of steelmaking-continuous casting scheduling and considering the principle of furnace matching and multiple refining stages,a constraint satisfaction model was established with the objectives of furnace-caster matching degree,the total waiting time and the advance/delay time of the cast.For its multi-objective characteristics,an improved fast elitist Non-Dominated Sorting Genetic Algorithm(NSGA-Ⅱ)algorithm with adaptive grid selection strategy was proposed,which overcame the problem of easily losing the useful information with traditional Pareto domination method.The experiments and compared algorithm were set up based on actual production data of various scales,and the results showed that the proposed improved NSGA-Ⅱalgorithm proposed was superior to the traditional NSGA-Ⅱ in terms of convergence,diversity of optimal solution set and computational efficiency based on the scheduling problems of steelmaking-continuous casting.
Aiming at the special process requirements of steelmaking-continuous casting scheduling and considering the principle of furnace matching and multiple refining stages,a constraint satisfaction model was established with the objectives of furnace-caster matching degree,the total waiting time and the advance/delay time of the cast.For its multi-objective characteristics,an improved fast elitist Non-Dominated Sorting Genetic Algorithm(NSGA-Ⅱ)algorithm with adaptive grid selection strategy was proposed,which overcame the problem of easily losing the useful information with traditional Pareto domination method.The experiments and compared algorithm were set up based on actual production data of various scales,and the results showed that the proposed improved NSGA-Ⅱalgorithm proposed was superior to the traditional NSGA-Ⅱ in terms of convergence,diversity of optimal solution set and computational efficiency based on the scheduling problems of steelmaking-continuous casting.
引文
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[2]WANG Bailin,LI Tieke,ZHANG Chunsheng,et al.Dynamic CSP based scheduling algorithm for steelmaking and continuous casting with conticaster breakdown[J].Computer Integrated Manufacturing Systems,2011,17(10):2185-2194(in Chinese).[王柏琳,李铁克,张春生,等.基于动态约束满足的考虑连铸机故障的炼钢连铸调度算法[J].计算机集成制造系统,2011,17(10):2185-2194.]
[3]TANG Qiuhua,ZHENG Peng,ZHANG Liping,et al.Scheduling multi-buffered steelmaking production via heuristic rules and memetic algorithm[J].Computer Integrated Manufacturing Systems[J],2015,21(11):2955-2936(in Chinese).[唐秋华,郑鹏,张利平,等.融合启发式规则和文化基因算法的多缓冲炼钢-连铸生产调度[J].计算机集成制造系统,2015,21(11):2955-2963.]
[4]MAO K,PAN Q K,PANG X,et al.A novel Lagrangian relaxation approach for a hybrid flowshop scheduling problem in the steelmaking-continuous casting process[J].European Journal of Operational Research,2014,236(1):51-60.
[5]WANG Gang,WANG Bin,WANG Bao,et al.Scheduling model for steelmaking-continuous casting process based on“furnace-caster matching”principle[J].Journal of University of Science and Technology Beijing,2013,35(8):1080-1092(in Chinese).[王刚,王彬,王宝,等.基于“炉机对应”原则的炼钢-连铸调度模型[J].北京科技大学学报,2013,35(8):1080-1092.]
[6]WANG Shanshan,ZENG Liang.Intelligent scheduling model and algorithm for steelmaking and continuous casting based on multi-objective optimization[J].Science Technology and Engineering,2015,15(34):88-94(in Chinese).[王珊珊,曾亮.基于多目标优化的炼钢-连铸生产智能调度模型与算法[J].科学技术与工程,2015,15(34):88-94.]
[7]SONG H X,ZHANG J X,GAO K,et al.Optimum charge plan of steelmaking continuous casting based on the modified multi-objective evolutionary algorithm[J].Advanced Materials Research,2013,697(8):3406-3411.
[8]MASHWANI W K,SALHI A,YENIAY O,et al.Hybrid non-dominated sorting genetic algorithm with adaptive operators selection[J].Applied Soft Computing,2017,56:1-18.
[9]SUKSONGHONG K,BOONLONG K,GOH K L.Multi-objective genetic algorithms for solving portfolio optimization problems in the electricity market[J].International Journal of Electrical Power&Energy Systems,2014,58(2):150-159.
[10]ZHANG H,ZHOU J,FANG N,et al.An efficient multiobjective adaptive differential evolution with chaotic neuron network and its application on long-term hydropower operation with considering ecological environment problem[J].International Journal of Electrical Power&Energy Systems,2013,45(1):60-70.
[11]CHENG J,YEN G G,ZHANG G.A grid-based adaptive multi-objective differential evolution algorithm[J].Information Sciences,2016,367-368:890-908.
[12]PAN Q K.An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling[J].European Journal of Operational Research,2016,250(3):702-714.
[13]XIE Chengwang,LI Kai,LIAO Guoyong.Improved NSGA2algorithm with differential evolution local search[J].Computer Science,2013,40(10):235-238(in Chinese).[谢承旺,李凯,廖国勇.一种带差分局部搜索的改进型NSGA2算法[J].计算机科学,2013,40(10):235-238.]
[14]KNOWLES J D,CORNE D.Properties of an adaptive archiving algorithm for storing nondominated vectors[J].IEEETransactions on Evolutionary Computation,2003,7(2):100-116.