The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency...The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.展开更多
This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for...This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for locating and setting of thyristor controlled series capacitor(TCSC) and static var compensator(SVC) using the multi-objective optimization approach named strength pareto multi-objective evolutionary algorithm(SPMOEA). Maximization of the static voltage stability margin(SVSM) and minimizations of real power losses(RPL) and load voltage deviation(LVD) are taken as the goals or three objective functions, when optimally locating multi-type FACTS devices. The performance and effectiveness of the proposed approach has been validated by the simulation results of the IEEE 30-bus and IEEE 118-bus test systems. The proposed approach is compared with non-dominated sorting particle swarm optimization(NSPSO) algorithm. This comparison confirms the usefulness of the multi-objective proposed technique that makes it promising for determination of combinatorial problems of FACTS devices location and setting in large scale power systems.展开更多
In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop pro...In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.展开更多
In order to improve our military ’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental ...In order to improve our military ’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e.,“web-based kill,” and the operation loop theory. Firstly, we pioneer the operation loop recommendation problem with operation ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model.Secondly, considering the case where there are multiple decision objectives for the combat ring recommendation problem,we propose for the first time a multi-objective optimization algorithm, the multi-objective ant colony evolutionary algorithm based on decomposition(MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition(MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives nondominated solution set for the multi-objective problem. Finally,compared with other classical multi-objective optimization algorithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume(HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders’ decision-making.展开更多
实数编码的多目标进化算法常使用模拟二进制交叉(simulated binary crossover,称SBX)算子.通过对SBX以及进化策略中变异算子进行对比分析,并引入进化策略中的离散重组算子,提出了一种正态分布交叉(normal distribution crossover,称NDX...实数编码的多目标进化算法常使用模拟二进制交叉(simulated binary crossover,称SBX)算子.通过对SBX以及进化策略中变异算子进行对比分析,并引入进化策略中的离散重组算子,提出了一种正态分布交叉(normal distribution crossover,称NDX)算子.首先在一维搜索空间实例中对NDX与SBX算子进行比较和分析,然后将NDX算子应用于Deb等人提出的稳态多目标进化算法ε-MOEA(ε-dominance based multiobjective evolutionary algorithm)中.采用NDX算子的ε-MOEA(记为ε-MOEA/NDX)算法在多目标优化标准测试集ZDT和DTLZ的10个函数上进行了实验比较.实验结果和分析表明,采用NDX的ε-MOEA所求得的Pareto最优解集质量明显优于经典算法ε-MOEA/SBX和NSGA-Ⅱ.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
为了准确、快速地求解电力系统环境经济调度(environmental economic dispatching,EED)问题,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)应用于电力调度领域,提出了基于MOEA/D...为了准确、快速地求解电力系统环境经济调度(environmental economic dispatching,EED)问题,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)应用于电力调度领域,提出了基于MOEA/D的多目标环境经济调度算法。该算法首先采用Tchebycheff法将整个EED Pareto最优前沿的逼近问题分解为一定数量的单目标优化子问题,然后利用差分进化同时求解这些子问题,并在算法中加入约束处理及归一化操作,以获得最优的带约束EED问题的调度方案。最后,应用模糊集理论为决策者提供最优折中解。对IEEE 30节点测试系统进行仿真计算,并与其它智能优化算法的调度方案对比。结果表明,该算法有效可行,且具有很好的收敛速度和求解精度。展开更多
基金Project(50775089)supported by the National Natural Science Foundation of ChinaProject(2007AA04Z190,2009AA043301)supported by the National High Technology Research and Development Program of ChinaProject(2005CB724100)supported by the National Basic Research Program of China
文摘The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.
文摘This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for locating and setting of thyristor controlled series capacitor(TCSC) and static var compensator(SVC) using the multi-objective optimization approach named strength pareto multi-objective evolutionary algorithm(SPMOEA). Maximization of the static voltage stability margin(SVSM) and minimizations of real power losses(RPL) and load voltage deviation(LVD) are taken as the goals or three objective functions, when optimally locating multi-type FACTS devices. The performance and effectiveness of the proposed approach has been validated by the simulation results of the IEEE 30-bus and IEEE 118-bus test systems. The proposed approach is compared with non-dominated sorting particle swarm optimization(NSPSO) algorithm. This comparison confirms the usefulness of the multi-objective proposed technique that makes it promising for determination of combinatorial problems of FACTS devices location and setting in large scale power systems.
基金supported by the National Key R&D Plan(2020YFB1712902)the National Natural Science Foundation of China(52075036).
文摘In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.
基金supported by the National Natural Science Foundation of China (72071206,71690233)the Science and Technology Innovation Program of Hunan Province (2020RC4046)。
文摘In order to improve our military ’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e.,“web-based kill,” and the operation loop theory. Firstly, we pioneer the operation loop recommendation problem with operation ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model.Secondly, considering the case where there are multiple decision objectives for the combat ring recommendation problem,we propose for the first time a multi-objective optimization algorithm, the multi-objective ant colony evolutionary algorithm based on decomposition(MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition(MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives nondominated solution set for the multi-objective problem. Finally,compared with other classical multi-objective optimization algorithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume(HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders’ decision-making.
文摘实数编码的多目标进化算法常使用模拟二进制交叉(simulated binary crossover,称SBX)算子.通过对SBX以及进化策略中变异算子进行对比分析,并引入进化策略中的离散重组算子,提出了一种正态分布交叉(normal distribution crossover,称NDX)算子.首先在一维搜索空间实例中对NDX与SBX算子进行比较和分析,然后将NDX算子应用于Deb等人提出的稳态多目标进化算法ε-MOEA(ε-dominance based multiobjective evolutionary algorithm)中.采用NDX算子的ε-MOEA(记为ε-MOEA/NDX)算法在多目标优化标准测试集ZDT和DTLZ的10个函数上进行了实验比较.实验结果和分析表明,采用NDX的ε-MOEA所求得的Pareto最优解集质量明显优于经典算法ε-MOEA/SBX和NSGA-Ⅱ.
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.
文摘为了准确、快速地求解电力系统环境经济调度(environmental economic dispatching,EED)问题,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)应用于电力调度领域,提出了基于MOEA/D的多目标环境经济调度算法。该算法首先采用Tchebycheff法将整个EED Pareto最优前沿的逼近问题分解为一定数量的单目标优化子问题,然后利用差分进化同时求解这些子问题,并在算法中加入约束处理及归一化操作,以获得最优的带约束EED问题的调度方案。最后,应用模糊集理论为决策者提供最优折中解。对IEEE 30节点测试系统进行仿真计算,并与其它智能优化算法的调度方案对比。结果表明,该算法有效可行,且具有很好的收敛速度和求解精度。
