基于多维EDA算法的低碳分布式装配流水车间调度

针对低碳分布式装配置换流水车间调度问题(LC＿DAPFSP),建立以同时最小化总能耗和总完工时间为优化目标的数学模型,进而提出一种多维分布估计算法(MEDA)以进行求解.首先,采用随机方法和启发式算法共同生成初始化种群;其次,建立基于矩阵立方体的概率模型,用于合理学习并积累优质解的块结构信息和序关系信息,同时设计有效采样机制对概率模型采样以生成新种群,从而合理引导算法搜索方向并发现可行解空间中的优质解区域;然后,为平衡算法的全局探索与局部开发能力,提出基于问题特性的变邻域局部搜索方法,可对全局搜索发现的优质解区域进行细致搜索;最后,通过仿真实验与算法对比验证MEDA是求解LC＿DAPFSP的有效算法.     

分布式有限缓冲区流水车间混合EDA调度北大核心CSCD

在实际工业生产背景下,针对具有NP难特性的分布式有限缓冲区流水车间调度问题,提出了一种混合分布估计算法,用于最小化最大完成时间。首先,由于已有算法无法保证局部搜索后概率模型对优质个体分布统计的准确性,提出了反最小完成工厂映射规则;然后,引入基于Swap邻域和基于Insert邻域的局部搜索,进一步加强算法的局部搜索能力,从而对HEDA全局搜索得到的优质解区域进行细致搜索;最后,通过仿真实验和算法的比较验证HEDA的有效性。     

增强分布估计算法求解低碳分布式流水线调度

针对低碳分布式流水线调度问题(DFSP–LC),提出了一种基于序关系的增强分布估计算法(OEEDA),用于最小化最大完成时间和总碳排放量.在OEEDA的第1阶段,利用基于贝叶斯统计推断的分布估计算法(BEDA)在问题解空间进行一定时间的搜索,用于发现优质解并将其保存于非劣解集中.在OEEDA的第2阶段,提出了基于序关系的四维矩阵(OFDM)对优质解的序关系(即工件块结构及其位置信息)进行有效学习和积累,进而设计了在解中固定部分块结构的采样机制,可更加明确地指导算法的全局搜索方向.同时,引入基于解、工厂间、工厂内的3种不同Insert融合的搜索方式,对2个阶段全局搜索得到的优质解区域进行较为细致的局部搜索.最后,通过仿真实验和算法对比验证了OEEDA的有效性.     

超启发式分布估计算法求解带软时间窗的同时取送货车辆路径问题

本文针对带软时间窗的同时取送货车辆路径问题(VRPSPDSTW),以最小化车辆行驶总里程和最大化服务准时率为优化目标,提出一种超启发式分布估计算法(HHEDA)进行求解.全局搜索阶段,首先,提出3种启发式规则生成初始个体,以确保初始种群的质量和分散性;其次,根据问题特点,构造3个概率矩阵分别学习和积累优质解的排序信息、客户间的距离信息和捆绑信息,并通过采样概率矩阵生成新个体,以增强算法全局搜索发现解空间中优质区域的能力.局部搜索阶段,将11种邻域操作组成备选集合,进而设计学习型超启发式局部搜索(LHHLS),用于动态选择备选集合中的部分邻域操作构成多种新的有效启发式算法,以执行对解空间中优质区域的深入搜索.最后,仿真实验和算法比较验证了HHEDA的有效性.     

分阶段二次变异的多目标混沌差分进化算法

提出一种结合分阶段二次变异和混沌理论的改进差分进化(DE)算法,以解决多目标约束优化问题.其核心思想是,在DE进化前期采用基于非支配解的随机二次变异来提高算法的全局寻优能力,进化后期采用基于非支配解的混沌二次变异来提高DE的局部寻优能力.通过对典型测试问题的仿真实验验证了所提出的算法能在全局搜索性能与局部搜索性能之间维持较好平衡,而且保持了DE算法的简洁性能,其收敛性、分布度和均衡性均优于标准DE.     

随机资源约束项目调度问题基于序的果蝇算法

针对项目活动工期为随机变量的资源约束项目调度问题,提出一种基于序的果蝇算法.为了实现随机环境下解的有效评价,提出一种预选机制,并采用基于序的最优计算量分配技术.为了使果蝇算法能够求解资源约束项目调度问题,采用交换操作执行果蝇算法的嗅觉搜索,并采用保优更新操作执行视觉搜索.为了均衡算法的局部搜索和全局搜索能力,在标准果蝇算法中引入了协作进化环节并采用两点交叉操作加以实现.在不同随机分布的情况下,采用标准测试集进行仿真测试.与现有算法的比较结果验证了所提预选机制和基于序的果蝇算法的有效性.     

一种基于柯氏复杂度的因果网络定向方法

针对碳限额交易机制下制造/再制造混合系统生产决策问题,以最大化总利润和最小化碳排放量为目标,考虑客户需求差异以及碳减排技术投入成本和收益的影响,建立多周期产品制造/再制造混合系统生产决策优化模型.根据模型特点,结合多种群协同进化思想,设计一个多种群混合布谷鸟算法(multi-population hybrid cuckoo search,MPHCS)进行求解.提出一种自定义偏好随机游走策略,增加优秀个体对全局搜索的影响,以优秀个体指导种群进化,实现各子种群信息共享;采用自适应公式更新步长,防止陷入局部最优;同时引入基于Logistic映射的混沌搜索,用于对MPHCS算法全局搜索发现的优质解区域进行精细搜索,提高局部搜索能力.最后通过实例仿真验证了所提出模型和算法的合理性和有效性.     

混合交叉熵算法求解复杂零等待流水线调度问题

针对制造行业中广泛存在的一类复杂零等待流水线调度问题, 即带序相关设置时间和释放时间的零等待 流水线调度问题(NFSSP SDSTs RTs), 建立问题的排序模型并提出一种混合交叉熵算法(HCEA)进行求解, 优化目 标为最小化总提前和延迟时间. 首先, 设计了一种基于问题性质的快速评价方法, 有效降低评价解的计算复杂度. 其 次, 采用交叉熵算法学习并积累优质解的结构特征, 建立概率模型对优质解的工件块分布进行有效地估计. 通过合 理的采样和更新方法, 实现对解空间中优质区域的全局搜索. 然后, 为提高算法搜索效率, 设计带两种搜索策略的快 速局部搜索方法, 对全局搜索发现的优质区域进行细致且深入的搜索. 最后, 仿真实验与算法对比验证了HCEA可 有效求解NFSSP SDSTs RTs.     

基于差分演化与猫群算法融合的群体智能算法

提出一种基于差分演化与猫群算法融合的群体智能算法。该算法基于猫群算法的两种行为模式,引进差分演化的思想,根据分组率随机把群体分成两个种群,一个种群执行猫群算法搜寻模式,另一种群执行差分变异模式,算法采用一种信息共享机制,使两个种群在搜索最优解时可以实现协同进化,信息交流。既实现了不同进化模式间的优势互补,又可以增加种群的多样性。对5个基准函数进行仿真实验并分别与DE和CSO进行比较,表明混合算法同时具有全局搜索和局部搜索最优解性能,收敛速度快,计算精度高,更适合用于求解高维复杂函数。     

超启发式交叉熵算法求解模糊分布式流水线绿色调度问题

针对实际生产过程中普遍存在的加工时间不确定性,采用模糊数表示工件的加工时间,以同时最小化模糊最大完工时间和模糊总能耗为优化目标,建立模糊分布式流水线绿色调度问题(green distributed permutation flow-shop scheduling problem with fuzzy processing time,GDPFSP_FPT)的模型,进而提出一种超启发式交叉熵算法(hyper-heuristic cross-entropy algorithm,HHCE)进行求解.首先,HHCE采用一种新颖的三角模糊数排序准则合理计算个体的目标函数值,可在算法搜索过程中较准确发现优质解区域;其次,HHCE在高层利用基于贡献率的评价方法确定8种特定邻域操作所构成的各排列的优劣,同时采用交叉熵(cross-entropy,CE)方法学习较优排列的信息并生成新排列,进而在低层把高层生成的每个排列作为一种启发式算法,对低层相应个体执行一系列邻域操作,以实现对问题解空间较多不同区域的搜索;然后,HHCE将基于非关键路径的节能策略用于对低层每代种群中的较优个体执行局部搜索,从而进一步提高算法获取低能耗非劣个体或解的能力;最后,仿真实验与算法对比表明,HHCE可有效求解GDPFSP_FPT.     

A novel hybrid DE–random search approach for unit commitment problem

Differential evolution (DE) is a population-based stochastic search algorithm, whose simple yet powerful and straightforward features make it very attractive for numerical optimization. DE uses a rather greedy and less stochastic approach to problem-solving than other evolutionary algorithms. DE combines simple arithmetic operators with the classical operators of recombination, mutation and selection to evolve from a randomly generated starting population to a final solution. Although global exploration ability of DE algorithm is adequate, its local exploitation ability is feeble and convergence velocity is too low and it suffers from the problem of untime convergence for multimodal objective function, in which search process may be trapped in local optima and it loses its diversity. Also, it suffers from the stagnation problem, where the search process may infrequently stop proceeding toward the global optimum even though the population has not converged to a local optimum or any other point. To improve the exploitation ability and global performance of DE algorithm, a novel and hybrid version of DE algorithm is presented in the proposed research. This research paper presents a hybrid version of DE algorithm combined with random search for the solution of single-area unit commitment problem. The hybrid DE–random search algorithm is tested with IEEE benchmark systems consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by experimental analysis, it has been found that proposed algorithm yields global results for the solution of unit commitment problem.

     

预制构件并行生产线资源配置与生产调度集成优化

并行生产线和特定工序生产资源共享模式可以显著改善客户满意度并节约成本.针对预制构件并行生产线资源配置与生产调度集成优化问题,基于分解策略和交替迭代优化思想,提出一种交替式混合果蝇-禁忌搜索算法(AHFOA＿TS)以最小化拖期惩罚费用.首先,通过快速启发式方法产生一较好初始解;然后,固定资源配置方案,为提高算法局部搜索能力,通过集成多种局部搜索方式,设计一种离散果蝇优化算法优化订单指派及调度方案;最后,固定订单指派及调度方案,为减少无效搜索次数,设计一种基于双层变异算子和精英劣解交叉策略的混合禁忌搜索算法以优化资源配置方案,如此两个阶段交替运行直至满足终止条件.此外,设计4种基于交替搜索框架的智能优化算法用于比较.计算结果表明, AHFOA＿TS算法能够更有效求解预制构件生产线资源配置和生产调度集成优化问题.     

An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers

This paper proposes an effective hybrid algorithm based on differential evolution (DE), namely HDE, to solve multi-objective permutation flow shop scheduling problem (MPFSSP) with limited buffers between consecutive machines, which is a typical NP-hard combinatorial optimization problem with strong engineering background. Firstly, to make DE suitable for solving scheduling problems, a largest-order-value (LOV) rule is presented to convert the continuous values of individuals in DE to job permutations. Secondly, after the DE-based exploration, an efficient local search, which is designed based on the landscape of MPFSSP with limited buffers, is applied to emphasize exploitation. Thus, not only does the HDE apply the parallel evolution mechanism of DE to perform effective exploration (global search) in the whole solution space, but it also adopts problem-dependent local search to perform thorough exploitation (local search) in the promising sub-regions. In addition, the concept of Pareto dominance is used to handle the updating of solutions in sense of multi-objective optimization. Moreover, the convergence property of HDE is analyzed by using the theory of finite Markov chain. Finally, simulations and comparisons based on benchmarks demonstrate the effectiveness and efficiency of the proposed HDE.     

Flexible job shop scheduling using hybrid differential evolution algorithms

This paper proposes hybrid differential evolution (HDE) algorithms for solving the flexible job shop scheduling problem (FJSP) with the criterion to minimize the makespan. Firstly, a novel conversion mechanism is developed to make the differential evolution (DE) algorithm that works on the continuous domain adaptive to explore the problem space of the discrete FJSP. Secondly, a local search algorithm based on the critical path is embedded in the DE framework to balance the exploration and exploitation by enhancing the local searching ability. In addition, in the local search phase, the speed-up method to find an acceptable schedule within the neighborhood structure is presented to improve the efficiency of whole algorithms. Extensive computational results and comparisons show that the proposed algorithms are very competitive with the state of the art, some new best known solutions for well known benchmark instances have even been found.     

Improved differential evolution algorithms for solving generalized assignment problem

This paper presents algorithms based on differential evolution (DE) to solve the generalized assignment problem (GAP) with the objective to minimize the assignment cost under the limitation of the agent capacity. Three local search techniques: shifting, exchange, and k-variable move algorithms are added to the DE algorithm in order to improve the solutions. Eight DE-based algorithms are presented, each of which uses DE with a different combination of local search techniques. The experiments are carried out using published standard instances from the literature. The best proposed algorithm using shifting and k-variable move as the local search (DE-SK) techniques was used to compare its performance with those of Bee algorithm (BEE) and Tabu search algorithm (TABU). The computational results revealed that the BEE and DE-SK are not significantly different while the DE-SK outperforms the TABU algorithm. However, even though the statistical test shows that DE-SK is not significantly different compared with the BEE algorithm, the DE-SK is able to obtain more optimal solutions (87.5%) compared to the BEE algorithm that can obtain only 12.5% optimal solutions. This is because the DE-SK is designed to enhance the search capability by improving the diversification using the DE's operators and the k-variable moves added to the DE can improve the intensification. Hence, the proposed algorithms, especially the DE-SK, can be used to solve various practical cases of GAP and other combinatorial optimization problems by enhancing the solution quality, while still maintaining fast computational time.     

一种新的混合优化算法及其在车间调度中的应用

提出了一种解决车间调度问题的新方法, 该方法将序优化思想融入巢分区算法框架, 采用"序比较"的方法进行算法的局部寻优. "序"的指数收敛性加快了巢分区算法的局部收敛速度, 从而提高了算法整体的优化效率. 最优计算量分配技术则依据在线数据对计算量进行合理的分配, 进一步提高算法的收敛速度和结果的可靠性. 混合算法继承了巢分区算法的全局搜索特性以及序优化的快速收敛性. 用该算法解决标准 Jobshop 调度问题, 并与序优化方法和模拟退火算法进行比较, 发现本文算法在收敛速度与优化质量方面均优于这些算法.     

基于混合概率解发掘算法的第四方物流弹复性网络设计

为了使第四方物流系统能够安全、有效地运作,研究基于弹复性的第四方物流网络设计问题。建立以网络构建成本为约束、弹复性为目标的优化模型,针对问题模型设计基于最优计算预算分配的混合概率解发掘算法。通过随机产生的不同规模问题,对混合概率解发掘算法进行性能评估,并将实验结果与传统的概率解发掘算法进行比较。实验结果表明了模型与算法的有效性。     

Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop

In this paper, we addressed two significant characteristics in practical casting production, namely tolerated time interval (TTI) and limited starting time interval (LimSTI). With the consideration of TTI and LimSTI, a multi-objective flexible job-shop scheduling model is constructed to minimize total overtime of TTI, total tardiness and maximum completion time. To solve this model, we present a hybrid discrete particle swarm optimization integrated with simulated annealing (HDPSO-SA) algorithm which is decomposed into global and local search phases. The global search engine based on discrete particle swarm optimization includes two enhancements: a new initialization method to improve the quality of initial population and a novel gBest selection approach based on extreme difference to speed up the convergence of algorithm. The local search engine is based on simulated annealing algorithm, where four neighborhood structures are designed under two different local search strategies to help the proposed algorithm jump over the trap of local optimal solution. Finally, computational results of a real-world case and simulation data expanded from benchmark problems indicate that our proposed algorithm is significant in terms of the quality of non-dominated solutions compared to other algorithms.     

Levy distributed parameter control in differential evolution for numerical optimization

Differential evolution (DE) algorithm is a population based stochastic search technique widely applied in scientific and engineering fields for global optimization over real parameter space. The performance of DE algorithm highly depends on the selection of values of the associated control parameters. Therefore, finding suitable values of control parameters is a challenging task and researchers have already proposed several adaptive and self-adaptive variants of DE. In the paper control parameters are adapted by levy distribution, named as Levy distributed DE (LdDE) which efficiently handles exploration and exploitation dilemma in the search space. In order to assure a fair comparison with existing parameter controlled DE algorithms, we apply the proposed method on number of well-known unimodal, basic and expanded multimodal and hybrid composite benchmark optimization functions having different dimensions. The empirical study shows that the proposed LdDE algorithm exhibits an overall better performance in terms of accuracy and convergence speed compared to five prominent adaptive DE algorithms.