A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective ...A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective differential evolution algorithm based on decomposition （MODE/D）. The two-objective and three-objective optimization experiments were performed respectively to demonstrate the optimal solutions of trade-off. The simulation results show that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to draft scheduling. The extreme Pareto solutions are found feasible and the centres of the Pareto fronts give a good compromise. The conflict exists between each two ones of three objectives. The final optimal solution is selected from the Pareto-optimal front by the importance of objectives, and it can achieve a better performance in all objective dimensions than the empirical solutions. Finally, the practical application cases confirm the feasibility of the multi-objective approach, and the optimal solutions can gain a better rolling stability than the empirical solutions, and strip flatness decreases from （0± 63） IU to （0±45） IU in industrial production.展开更多
Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose...Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories（4DTs）（3D position and time） for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategiclevel conflict management is developed in this paper.Specifically,a bi-objective N4 DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm（MOMMA） that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4 DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.展开更多
为了准确地求解组合权重的组合系数,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)思想引入评估领域,提出一种基于MOEA/D的组合权重方法.通常,利用加权和法将组合权重模型转化为...为了准确地求解组合权重的组合系数,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)思想引入评估领域,提出一种基于MOEA/D的组合权重方法.通常,利用加权和法将组合权重模型转化为单目标模型时,模型加权系数难以准确确定.对此,引入MOEA/D算法的分解思想,将组合权重模型转化为多个单目标子模型.MOEA/D算法仅适用于无约束优化问题,而较为常用的惩罚函数法难以表达进化初期无可行解的情况,因而提出改进自适应惩罚函数(improved adaptive penalty function,IAPF),将组合权重模型转化为无约束优化模型.应用所提出方法与其他方法进行仿真实验,实验结果表明,所提出算法具有有效性.展开更多
建立含风电系统的环境经济调度(Environmental Economic Dispatch,EED)模型,提出采用基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)来求解,并在算法中加入约束处理,归一化及变异操...建立含风电系统的环境经济调度(Environmental Economic Dispatch,EED)模型,提出采用基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)来求解,并在算法中加入约束处理,归一化及变异操作来改善算法的性能并保持解的多样性,以获得理想的Pareto最优前沿.通过仿真计算,并与其他优化算法进行对比分析,验证了MOEA/D算法解决含风电EED问题的可行性和有效性.展开更多
基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)是多目标优化算法的一个重要分支,多目标优化的关键问题是如何在算法的收敛性和散布性之间达到良好的平衡.目前主流算法的聚合函数存...基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)是多目标优化算法的一个重要分支,多目标优化的关键问题是如何在算法的收敛性和散布性之间达到良好的平衡.目前主流算法的聚合函数存在着不同的优缺点,尤其是当使用切比雪夫方法选择个体时,经常出现个体偏离权重现象,个体和权重间得不到很好的粘合.本文基于此提出了一种新的聚合函数方法,提高了MOEA/D的性能.该聚合函数的函数形式为二次函数,种群个体在该函数下的等高线是一条二次曲线(本文称双曲线函数方法,Hyperbola Function Method,HYB),是对目前存在的聚合函数的一种泛化形式.该HYB方法相比PBI(Penalty-based Boundary Intersection)方法更强调收敛性,能更容易地在收敛性散布性之间达到平衡.本文测试了MOKP问题及DTLZ系列等测试函数,并与其他算法进行了实验对比,结果显示HYB方法更稳定有效且种群在收敛速度上有一定的提高.展开更多
Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model base...Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.展开更多
针对具有不同加工流程信息的多类型零件的单元构建问题,建立了最大化机器利用率和成组效率的多目标单元构建数学规划模型。在此基础上,提出一种改进MOEA/D算法(improved multi-objective evolutionary algorithm based on decomposition...针对具有不同加工流程信息的多类型零件的单元构建问题,建立了最大化机器利用率和成组效率的多目标单元构建数学规划模型。在此基础上,提出一种改进MOEA/D算法(improved multi-objective evolutionary algorithm based on decomposition,IMOEA/D)。剖析模型特征,设计了面向机器分配和零件划分的双层编码策略;为了保证算法迭代的有效性,设计了初始化筛选方法和考虑各制造单元间机器零件平衡性的非法解修复策略;为了增强算法的局部探索能力,设计了基于模拟退火算法的局部搜索方法。实验结果表明所提算法具有优越的性能,获得的Pareto前沿解在覆盖率和Pareto比率两个指标上表现较优,且随着问题规模的扩大,其Pareto前沿优势更加明显。展开更多
为提高微电网应对新能源出力和负荷不确定性的能力,提出1种考虑灵活性资源的微电网优化控制策略。根据各类灵活性资源的源储荷特性进行分类,建立微电网双层优化调度模型。用户层引入用户侧灵活性资源,以用户费用和净负荷差值最小为优化...为提高微电网应对新能源出力和负荷不确定性的能力,提出1种考虑灵活性资源的微电网优化控制策略。根据各类灵活性资源的源储荷特性进行分类,建立微电网双层优化调度模型。用户层引入用户侧灵活性资源,以用户费用和净负荷差值最小为优化目标,决策变量为电动汽车和可平移负荷出力功率。源储层模型加入储能侧与发电侧灵活性资源,以微电网运营商成本和失负荷率最小为优化目标,决策变量为燃气轮机、主网联络线和储能单元出力功率。使用场景缩减的季节典型日数据进行算例仿真,采用改进后的基于分解的多目标进化MOEA/D(multi-objective evolutionary algorithm based on decomposition)算法对双层优化调度模型进行求解,年均用户费用降低6.85%,运营商年均总成本下降14.68%,年均失负荷率下降6.65%,验证了本文所提模型的合理性和有效性。展开更多
基金Projects(50974039,50634030)supported by the National Natural Science Foundation of China
文摘A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective differential evolution algorithm based on decomposition （MODE/D）. The two-objective and three-objective optimization experiments were performed respectively to demonstrate the optimal solutions of trade-off. The simulation results show that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to draft scheduling. The extreme Pareto solutions are found feasible and the centres of the Pareto fronts give a good compromise. The conflict exists between each two ones of three objectives. The final optimal solution is selected from the Pareto-optimal front by the importance of objectives, and it can achieve a better performance in all objective dimensions than the empirical solutions. Finally, the practical application cases confirm the feasibility of the multi-objective approach, and the optimal solutions can gain a better rolling stability than the empirical solutions, and strip flatness decreases from （0± 63） IU to （0±45） IU in industrial production.
基金co-supported by the National Science Foundation for Young Scientists of China(No.61401011)the National Key Technologies R&D Program of China(No.2015BAG15B01)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.61521091)
文摘Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories（4DTs）（3D position and time） for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategiclevel conflict management is developed in this paper.Specifically,a bi-objective N4 DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm（MOMMA） that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4 DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.
文摘为了准确地求解组合权重的组合系数,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)思想引入评估领域,提出一种基于MOEA/D的组合权重方法.通常,利用加权和法将组合权重模型转化为单目标模型时,模型加权系数难以准确确定.对此,引入MOEA/D算法的分解思想,将组合权重模型转化为多个单目标子模型.MOEA/D算法仅适用于无约束优化问题,而较为常用的惩罚函数法难以表达进化初期无可行解的情况,因而提出改进自适应惩罚函数(improved adaptive penalty function,IAPF),将组合权重模型转化为无约束优化模型.应用所提出方法与其他方法进行仿真实验,实验结果表明,所提出算法具有有效性.
文摘建立含风电系统的环境经济调度(Environmental Economic Dispatch,EED)模型,提出采用基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)来求解,并在算法中加入约束处理,归一化及变异操作来改善算法的性能并保持解的多样性,以获得理想的Pareto最优前沿.通过仿真计算,并与其他优化算法进行对比分析,验证了MOEA/D算法解决含风电EED问题的可行性和有效性.
文摘基于分解的多目标进化算法(Multi-objective Evolutionary Algorithm Based on Decomposition,MOEA/D)是多目标优化算法的一个重要分支,多目标优化的关键问题是如何在算法的收敛性和散布性之间达到良好的平衡.目前主流算法的聚合函数存在着不同的优缺点,尤其是当使用切比雪夫方法选择个体时,经常出现个体偏离权重现象,个体和权重间得不到很好的粘合.本文基于此提出了一种新的聚合函数方法,提高了MOEA/D的性能.该聚合函数的函数形式为二次函数,种群个体在该函数下的等高线是一条二次曲线(本文称双曲线函数方法,Hyperbola Function Method,HYB),是对目前存在的聚合函数的一种泛化形式.该HYB方法相比PBI(Penalty-based Boundary Intersection)方法更强调收敛性,能更容易地在收敛性散布性之间达到平衡.本文测试了MOKP问题及DTLZ系列等测试函数,并与其他算法进行了实验对比,结果显示HYB方法更稳定有效且种群在收敛速度上有一定的提高.
文摘Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.
文摘针对具有不同加工流程信息的多类型零件的单元构建问题,建立了最大化机器利用率和成组效率的多目标单元构建数学规划模型。在此基础上,提出一种改进MOEA/D算法(improved multi-objective evolutionary algorithm based on decomposition,IMOEA/D)。剖析模型特征,设计了面向机器分配和零件划分的双层编码策略;为了保证算法迭代的有效性,设计了初始化筛选方法和考虑各制造单元间机器零件平衡性的非法解修复策略;为了增强算法的局部探索能力,设计了基于模拟退火算法的局部搜索方法。实验结果表明所提算法具有优越的性能,获得的Pareto前沿解在覆盖率和Pareto比率两个指标上表现较优,且随着问题规模的扩大,其Pareto前沿优势更加明显。
文摘为提高微电网应对新能源出力和负荷不确定性的能力,提出1种考虑灵活性资源的微电网优化控制策略。根据各类灵活性资源的源储荷特性进行分类,建立微电网双层优化调度模型。用户层引入用户侧灵活性资源,以用户费用和净负荷差值最小为优化目标,决策变量为电动汽车和可平移负荷出力功率。源储层模型加入储能侧与发电侧灵活性资源,以微电网运营商成本和失负荷率最小为优化目标,决策变量为燃气轮机、主网联络线和储能单元出力功率。使用场景缩减的季节典型日数据进行算例仿真,采用改进后的基于分解的多目标进化MOEA/D(multi-objective evolutionary algorithm based on decomposition)算法对双层优化调度模型进行求解,年均用户费用降低6.85%,运营商年均总成本下降14.68%,年均失负荷率下降6.65%,验证了本文所提模型的合理性和有效性。
