基于文化算法的无等待Flowshop调度

针对目标函数为最小化最大完工时间的无等待流水车间调度问题,提出了基于文化算法思想的混合遗传算法.该算法利用文化算法的知识记忆思想进行解群体的控制,构造了两个记忆器来实现这一功能,在迭代过程中继承上一代中较优解的特性,提高了搜索效率和搜索质量.算例实验证明了该算法的有效性.     

基于混合离散人工蜂群算法的混合零等待柔性流水车间优化研究

从钢铁业等流程工业提炼出一类混合零等待柔性流水车间问题，其中一些加工阶段要求工件连续不断地经过这些工序，对该问题建立了整数规划模型，提出了一种混合离散人工蜂群算法以最小化最大完工时间。采用二维矩阵编码表述染色体以及工件右移调整策略进行解码以获取调度解，改进NEH启发式规则用于生成初始种群。在雇佣蜂阶段，引入了修正粒子群优化算法产生新解；在跟随蜂阶段，设计了迭代贪婪算法中的破坏和构造算子，进一步增强算法的搜索能力；在侦查蜂阶段，利用变邻域搜索算子以替换最差解。对不同规模问题进行了仿真测试并与现有算法进行对比，结果表明所提算法在求解混合零等待柔性流水车间问题方面更加有效。     

考虑准备时间的多目标混合流水车间调度

本文研究了一个带有不可预期发生且准备时间顺序相关的混合流水车间调度问题,以最小化制造期和总拖期为多目标进行Pareto求解。首先建立了一个混合整数线性规划模型,然后提出了一种NEH-Pareto档案模拟退火(NEH-pareto archive simulated annealing,NEH-PASA)融合算法,算法采用一种改进的NEH算法产生高质量的初始解,设计了一种基于Pareto最优的混合扰动策略生成邻域解,并引入一种Pareto搜索机制以获取Pareto解集。最后通过计算实验,验证了算法的优越性。     

置换流水车间调度问题的萤火虫算法求解

作为新兴的仿生群智能优化算法,分析了萤火虫算法的仿生原理,对算法实现优化过程进行了定义。针对最小化最大完工时间的置换流水车间调度问题,采用基于ROV规则的随机键编码方式和互换操作的局部搜索策略,应用萤火虫算法进行求解。通过典型实例对算法进行了仿真测试,调度结果表明了萤火虫算法求解置换流水车间调度问题的可行性和有效性,优于NEH启发式算法和粒子群算法,是解决流水线生产调度问题的一种有效方法。     

基于绿色生产的混合流水车间调度问题研究

针对绿色可持续发展问题,通过量化绿色指标评价方法,构建最小化最大完工时间、碳排放和噪声的多目标混合流水车间调度模型,并提出一种混合离散多目标帝国竞争算法(hybrid discrete multi-objective imperial competition algorithm,HDMICA)对模型进行求解。采用基于混沌反向学习策略的种群初始化方式提高初始化种群的多样性;基于本文模型设计3种有效的局部搜索策略以提升算法局部搜索能力;通过实验验证所提算法的有效性及优越性。     

有限等待流水车间调度邻域搜索算法

针对工序间等待时间受限,目标函数为最大完工时间最短的流水车间调度问题,提出了一种动态变邻域搜索算法。算法采用工件对比较算法和贪婪插入规则,构建了初始调度;通过嵌入3-opt,2-opt实现动态变邻域搜索;并在迭代过程中加入动态禁忌策略。     

改进麻雀算法求解低碳柔性作业车间调度问题

针对车间调度对制造业能源消耗和碳排放影响较大的问题,建立以最小化最大完工时间和碳排放量为目标的低碳柔性作业车间调度模型,采用改进的麻雀搜索算法求解。首先,通过三种不同的搜索方式对种群进行初始化,保证初始种群的质量。其次,引入正弦搜索策略,使个体根据自身位置的优劣采用不同的搜索策略,增强算法的搜索能力。再次,引入交叉和变异算子,避免算法迅速陷入局部最优。最后,通过Brandimarte数据集和实例仿真验证改进算法的性能。     

零等待混合流水车间问题优化研究

研究了带零等待的混合流水车间调度问题,考虑工件动态到达的实际生产特征,以最小化总加权完成时间为目标,建立整数规划模型,然后设计一种基于代理次梯度法的改进拉格朗日松弛算法.基于工件分解策略将拉格朗日松弛问题分解为多个工件级子问题,不同于每次迭代要求最优求解所有子问题的次梯度法,所设计的代理次梯度法通过每次迭代最优求解几个子问题得到松弛问题的近似解,进而获得搜索拉格朗日乘子的代理次梯度方向,最后设计启发式构造可行时间表.通过仿真实验,证明了所设计的算法在解的质量和收敛性方面均优于传统的使用次梯度法的拉格朗日松弛算法.     

基于改进Jaya算法的置换流水车间调度问题研究

置换流水车间调度问题（permutation flow shop scheduling problem, PFSP）广泛存在于流程和离散制造企业。本文提出一种改进的Jaya算法求解最小化最大完工时间为目标的PFSP。在改进Jaya算法中,设计了基于最优和最差个体的4种个体更新方案,通过4种邻域结构对个体进行局部搜索,并通过多样性控制策略来保证种群的多样性。采用改进Jaya算法分解求解Car、 Rec和Taillard基准问题,并与其他算法进行比较,验证了所提算法的有效性。     

基于工序集的多阶段混合流水车间调度问题研究北大核心

针对砂型铸造车间包含并行工序集与批处理集的多阶段调度问题,总结了该类问题的特点和难点,构建了以最小化最大完工时间为优化目标的多阶段混合流水车间调度模型,采用了一种改进人工蜂群算法求解该模型。在算法中提出了基于插入原理与前驱工序释放时间的分段解码方法来有效利用机器空闲时间段,并引入了动态触发邻域机制增强算法的局部搜索能力,最后通过仿真实验验证了本文算法,解决此类问题的可行性和有效性。     

基于粒子群与聚类的多目标优化算法

针对粒子群优化算法容易陷入局部最优的问题,提出了一种基于粒子群优化与分解聚类方法相结合的多目标优化算法。算法基于参考向量分解的方法,通过聚类优选粒子策略来更新全局最优解。首先,通过每条均匀分布的参考向量对粒子进行聚类操作,来促进粒子的多样性。从每个聚类中选择一个具有最小聚合函数适应度值的粒子,以平衡收敛性和多样性。动态更新全局最优解和个体最优解,引导种群均匀分布在帕累托前沿附近。通过仿真实验,与4种粒子群多目标优化算法进行对比。实验结果表明,提出的算法在27个选定的基准测试问题中获得了20个反世代距离(IGD)最优值。     

一种基于“探测"与“开采"的多目标进化算法

针对实践中多目标优化问题(MOPs)的Pareto解集(PS)未知且比较复杂的特性,提出了一种基于"探测"(Exploration)与"开采"(Exploitation)的多目标进化算法(MOEA)——MOEA/2E。该算法在进化过程中采用"探测"与"开采"相结合的方法,用进化操作不断地探测新的搜索区域,用局部搜索充分开采优秀的解区域,并用隐最优个体保留机制保存每一代的最优个体。与目前最流行且有效的多目标进化算法NSGA-Ⅱ及SPEA-Ⅱ进行的比较实验结果表明,MOEA/2E获得的Pareto最优解集具有更好的收敛性与分布性。     

MOEA/D-SQA: a multi-objective memetic algorithm based on decomposition

A multi-objective memetic algorithm based on decomposition is proposed in this article, in which a simplified quadratic approximation (SQA) is employed as a local search operator for enhancing the performance of a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The SQA is used for a fast local search and the MOEA/D is used as the global optimizer. The multi-objective memetic algorithm based on decomposition, i.e. a hybrid of the MOEA/D with the SQA (MOEA/D-SQA), is designed to balance local versus global search strategies so as to obtain a set of diverse non-dominated solutions as quickly as possible. The emphasis of this article is placed on demonstrating how this local search scheme can improve the performance of MOEA/D for multi-objective optimization. MOEA/D-SQA has been tested on a wide set of benchmark problems with complicated Pareto set shapes. Experimental results indicate that the proposed approach performs better than MOEA/D. In addition, the results obtained are very competitive when comparing MOEA/D-SQA with other state-of-the-art techniques.     

基于速度交流的多种群多目标粒子群算法研究

为提高多目标优化算法的收敛精度和搜索性能,提出一种基于速度交流的多种群多目标粒子群算法。算法引入速度交流机制,将种群划分为多个子种群以实现速度信息共享,改善粒子单一搜索模式,提高算法的全局搜索能力。采用混沌映射优化惯性权重,提高粒子搜索遍历性和全局性,为降低算法在运行后期陷入局部最优Pareto前沿的可能性,对各个子种群执行不同的变异操作。将算法与NSGA-Ⅱ、SPEA2、AbYSS、MOPSO、SMPSO和GWASF-GA先进多目标优化算法进行对比,实验结果表明:该算法得到的解集具有更好的收敛性和分布性。     

A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

The safety hazards existing in the process of disassembling waste products pose potential harms to the physical and mental health of the workers. In this article, these hazards involved in the disassembly operations are evaluated and taken into consideration in a disassembly line balancing problem. A multi-objective mathematical model is constructed to minimise the number of workstations, maximise the smoothing rate and minimise the average maximum hazard involved in the disassembly line. Subsequently, a Pareto firefly algorithm is proposed to solve the problem. The random key encoding method based on the smallest position rule is used to adapt the firefly algorithm to tackle the discrete optimisation problem of the disassembly line balancing. To avoid the search being trapped in a local optimum, a random perturbation strategy based on a swap operation is performed on the non-inferior solutions. The validity of the proposed algorithm is tested by comparing with two other algorithms in the existing literature using a 25-task phone disassembly case. Finally, the proposed algorithm is applied to solve a refrigerator disassembly line problem based on the field investigation and a comparison of the proposed Pareto firefly algorithm with another multi-objective firefly algorithm in the existing literature is performed to further identify the superior performance of the proposed Pareto firefly algorithm, and eight Pareto optimal solutions are obtained for decision makers to make a decision.     

A Pareto-based estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem

To solve the multi-objective flexible job-shop problem (MFJSP), an effective Pareto-based estimation of distribution algorithm (P-EDA) is proposed. The fitness evaluation based on Pareto optimality is employed and a probability model is built with the Pareto superior individuals for estimating the probability distribution of the solution space. In addition, a mechanism to update the probability model is proposed, and the new individuals are generated by sampling the promising searching region based on the probability model. To avoid premature convergence and enhance local exploitation, the population is divided into two sub-populations at certain generations according to a splitting criterion, and different operators are designed for the two sub-populations to generate the promising neighbour individuals. Moreover, multiple strategies are utilised in a combination way to generate the initial solutions, and a local search strategy based on critical path is proposed to enhance the exploitation ability. Furthermore, the influence of parameters is investigated based on the Taguchi method of design of experiment, and a suitable parameter setting is suggested. Finally, numerical simulation based on some well-known benchmark instances and comparisons with some existing algorithms are carried out. The comparative results demonstrate the effectiveness of the proposed P-EDA in solving the MFJSP.     

A hybrid evolutionary graph-based multi-objective algorithm for layout optimization of truss structures

In this paper a new graph-based evolutionary algorithm, gM-PAES, is proposed in order to solve the complex problem of truss layout multi-objective optimization. In this algorithm a graph-based genotype is employed as a modified version of Memetic Pareto Archive Evolution Strategy (M-PAES), a well-known hybrid multi-objective optimization algorithm, and consequently, new graph-based crossover and mutation operators perform as the solution generation tools in this algorithm. The genetic operators are designed in a way that helps the multi-objective optimizer to cover all parts of the true Pareto front in this specific problem. In the optimization process of the proposed algorithm, the local search part of gM-PAES is controlled adaptively in order to reduce the required computational effort and enhance its performance. In the last part of the paper, four numeric examples are presented to demonstrate the performance of the proposed algorithm. Results show that the proposed algorithm has great ability in producing a set of solutions which cover all parts of the true Pareto front.     

用于结构优化设计的改进多目标群搜索算法

在多目标群搜索算法（multi-objective group search optimization, MGSO）基本原理的基础上,结合Pareto最优解理论,提出了基于约束改进的多目标群搜索算法（IMGSO）,并应用于多目标的结构优化设计．算法的改进主要有3个方面：第一,引入过渡可行域的概念来处理约束条件;第二,利用庄家法来构造非支配解集;最后,结合禁忌搜索算法和拥挤距离机制来选择发现者,以避免解集过早陷入局部最优,并提高收敛精度．采用IMGSO优化算法分别对平面和空间桁架结构进行了离散变量的截面优化设计,并与MGSO优化算法的计算结果进行了比较,结果表明改进的多目标群搜索优化算法IMGSO与MGSO算法相比具有更好的收敛精度.通过算例表明：IMGSO算法得到的解集中的解能大部分支配MGSO算法的解,在复杂高维结构中IMGSO算法的优越性更加明显,且收敛速度也有一定的提高,可有效应用于多目标的实际结构优化设计．     

A hybrid Pareto-based local search algorithm for multi-objective flexible job shop scheduling problems

This paper presents a hybrid Pareto-based local search (PLS) algorithm for solving the multi-objective flexible job shop scheduling problem. Three minimisation objectives are considered simultaneously, i.e. the maximum completion time (makespan), the total workload of all machines, and the workload of the critical machine. In this study, several well-designed neighbouring approaches are proposed, which consider the problem characteristics and thus can hold fast convergence ability while keep the population with a certain level of quality and diversity. Moreover, a variable neighbourhood search (VNS) based self-adaptive strategy is embedded in the hybrid algorithm to utilise the neighbouring approaches efficiently. Then, an external Pareto archive is developed to record the non-dominated solutions found so far. In addition, a speed-up method is devised to update the Pareto archive set. Experimental results on several well-known benchmarks show the efficiency of the proposed hybrid algorithm. It is concluded that the PLS algorithm is superior to the very recent algorithms, in term of both search quality and computational efficiency.     

A multi-objective genetic algorithm approach to the design of cellular manufacturing systems

In this paper, a multi-objective integer programming model is constructed for the design of cellular manufacturing systems with independent cells. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. There are some features that make the proposed algorithm different from other algorithms used in the design of cellular manufacturing systems. These include: (1) a systematic uniform design-based technique, used to determine the search directions, and (2) searching the solution space in multiple directions instead of single direction. Four problems are selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method in designing the manufacturing cells.