A hybrid variable neighborhood search algorithm for solving the limited-buffer permutation flow shop scheduling problem with the makespan criterion

This paper investigates the limited-buffer permutation flow shop scheduling problem (LBPFSP) with the makespan criterion. A hybrid variable neighborhood search (HVNS) algorithm hybridized with the simulated annealing algorithm is used to solve the problem. A method is also developed to decrease the computational effort needed to implement different types of local search approaches used in the HVNS algorithm. Computational results show the higher efficiency of the HVNS algorithm as compared with the state-of-the-art algorithms. In addition, the HVNS algorithm is competitive with the algorithms proposed in the literature for solving the blocking flow shop scheduling problem (i.e., LBPFSP with zero-capacity buffers), and finds 54 new upper bounds for the Taillard's benchmark instances.     

求解护士排班问题的变邻域搜索算法

变邻域搜索算法是求解护士排班问题的一个有效算法,其扰动方法对算法性能有显著影响。为提高护士排班问题中护士的满意度,提出一个改进的变邻域搜索(IVNS)算法。该算法使用了三种邻域结构,而且当使用任意的邻域都不能进一步改进当前解时,设计了一个对当前最优解进行扰动的方法,即在排班期间内随机地选择两天,在不违反硬性约束的条件下选出一组值班护士并交换他们在这两天中的班次。在2010年举行的第一次全球护士排班大赛提供的一组公共测试集上与一个混合变邻域搜索(HVNS)算法进行了比较,在Sprint-early、Medium-early和Long-early组算例上的结果表明,IVNS算法的最优值至少不劣于HVNS,而平均值均优于HVNS;IVNS算法的最大方差为0.72,波动范围小,求解性能稳定。IVNS的扰动方案对现有方案的扰动较小,能有效跳出当前局部最优,增强变邻域搜索算法的优化能力,与HVNS算法相比,其求解性能更优。     

自适应动态邻域布谷鸟混合算法求解TSP问题

针对离散布谷鸟算法求解旅行商问题时邻域搜索效率低和易陷入局部最优解等问题,提出了一种自适应动态邻域布谷鸟混合算法（Adaptive Dynamic Neighborhood Hybrid Cuckoo Search algorithm,ADNHCS）。为了提升邻域搜索效率,设计了一种圆限定突变的动态邻域结构来降低经典算法的随机性;此外,提出了可根据迭代过程进行自适应参数调整的策略,并结合禁忌搜索算法来提升全局寻优的能力。使用MATLAB和标准TSPLIB数据库中的若干经典算例对算法性能进行了实验仿真,结果表明与其他基于布谷鸟算法、经典和新型群智能优化算法相比,ADNHCS算法在全局寻优能力以及稳定性方面表现更优。     

基于AHTPSO的连续属性离散化算法

针对粗糙集不能较好地处理连续型属性的问题,结合粗糙集理论和粒子群算法,提出基于自适应混合禁忌搜索粒子群的连续属性离散化算法。首先,该算法通过对参数的自适应更新操作,从而避免了粒子群出现早熟的现象;然后将粒子群当代得到的全局最优粒子送入禁忌算法中进行优化,有效地提升了算法的局部探索能力;在兼顾决策表系统一致性的同时,将划分的断点初始化为一群随机粒子,通过改进后粒子群的自我迭代得到最佳的离散化划分点。实验结果表明,与其他结合粗糙集的离散化算法相比,该算法具有更高的规则分类精度和较少的离散化断点个数,对连续属性的离散化效果较好。     

混合灰狼优化算法求解柔性作业车间调度问题

将灰狼优化算法(GWO)用于柔性作业车间调度问题(FJSP),以优化最大完工时间为目标,提出一种混合灰狼优化算法(HGWO).首先,采用两段式编码方式,建立GWO连续空间与FJSP离散空间的映射关系;其次,设计种群初始化方法,保证算法初始解的质量;然后,嵌入一种变邻域搜索策略,加强算法的局部搜索能力,引入遗传算子,提升算法的全局探索能力;最后,通过实验数据验证HGWO算法在求解FJSP问题方面的有效性.     

A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization

The difficulties associated with using classical mathematical programming methods on complex optimization problems have contributed to the development of alternative and efficient numerical approaches. Recently, to overcome the limitations of classical optimization methods, researchers have proposed a wide variety of meta-heuristics for searching near-optimum solutions to problems. Among the existing meta-heuristic algorithms, a relatively new optimization paradigm is the Shuffled Complex Evolution at the University of Arizona (SCE-UA) which is a global optimization strategy that combines concepts of the competition evolution theory, downhill simplex procedure of Nelder-Mead, controlled random search and complex shuffling. In an attempt to reduce processing time and improve the quality of solutions, particularly to avoid being trapped in local optima, in this paper is proposed a hybrid SCE-UA approach. The proposed hybrid algorithm is the combination of SCE-UA (without Nelder-Mead downhill simplex procedure) and a pattern search approach, called SCE-PS, for unconstrained optimization. Pattern search methods are derivative-free, meaning that they do not use explicit or approximate derivatives. Moreover, pattern search algorithms are direct search methods well suitable for the global optimization of highly nonlinear, multiparameter, and multimodal objective functions. The proposed SCE-PS method is tested with six benchmark optimization problems. Simulation results show that the proposed SCE-PS improves the searching performance when compared with the classical SCE-UA and a genetic algorithm with floating-point representation for all the tested problems. As evidenced by the performance indices based on the mean performance of objective function in 30 runs and mean of computational time, the SCE-PS algorithm has demonstrated to be effective and efficient at locating best-practice optimal solutions for unconstrained optimization.     

一种新的混合量子进化算法

量子进化算法(QEA)用于多峰函数优化时,容易陷入局部最优.本文提出一种新的混合量子进化算法,通过双编码机制(经典二进制编码和量子概率编码),以及经典交叉和量子概率编码更新策略,实现了经典遗传算法与量子进化算法的有机结合,在发挥经典遗传算法全局优化能力的同时,利用量子概率搜索提高了算法的局部搜索能力.通过一组典型函数优化实验对该算法的性能进行了考察,并与QEA进行了比较.结果表明,本文算法在解的质量和收敛速度上都要优于QEA.     

解决二次分配问题的改进蚁群算法

现有的解决二次分配问题的蚁群算法大都与局部搜索过程相结合,文章对其中的局部搜索过程做了修改:一方面结合利用包含全局信息的信息素来指导局部搜索,避免了快速陷入局部最优;另一方面加入了一个二次机会策略,充分搜索解邻域,增强了算法的搜索能力。运用该文给出的算法,针对QAPLIB(二次分配基准问题库)中的问题进行了计算,并将结果与原有蚁群算法进行了比较。实验结果表明该文提出的算法具有更优的性能。     

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.     

A hybrid metaheuristic approach for the capacitated p-median problem

The capacitated p-median problem (CPMP) seeks to obtain the optimal location of p medians considering distances and capacities for the services to be given by each median. This paper presents an efficient hybrid metaheuristic algorithm by combining a proposed cutting-plane neighborhood structure and a tabu search metaheuristic for the CPMP. In the proposed neighborhood structure to move from the current solution to a neighbor solution, an open median is selected and closed. Then, a linear programming (LP) model is generated by relaxing binary constraints and adding new constraints. The generated LP solution is improved using cutting-plane inequalities. The solution of this strong LP is considered as a new neighbor solution. In order to select an open median to be closed, several strategies are proposed. The neighborhood structure is combined with a tabu search algorithm in the proposed approach. The parameters of the proposed hybrid algorithm are tuned using design of experiments approach. The proposed algorithm is tested on several sets of benchmark instances. The statistical analysis shows efficiency and effectiveness of the hybrid algorithm in comparison with the best approach found in the literature.     

聚类问题的自适应杂交差分演化模拟退火算法

针对K-均值聚类算法对初始值敏感和易陷入局部最优的缺点,提出了一个基于自适应杂交差分演化模拟退火的K-均值聚类算法。该算法以差分演化算法为基础,通过模拟退火算法的更新策略来增强全局搜索能力,并运用自适应技术来选择学习策略、确定算法的关键参数。实验结果表明,该算法能较好地克服传统K-均值聚类算法的缺点,具有较好的全局收敛能力,且算法稳定性强、收敛速度快,将新算法与传统的K-均值聚类算法以及最近提出的几个同类聚类算法进行了比较。     

一种动态分级的混合粒子群优化算法

针对粒子群算法早熟收敛和搜索精度不高的问题,提出一种动态分级的混合粒子群优化算法．该算法采取３种级别的并行粒子群算法,分别用于全局搜索和局部搜索及二者的结合,并根据搜索阶段动态调整各种级别中并行变量的数目．在全局搜索中,将混沌机制引入算法中以增强算法的全局搜索能力;在局部搜索中,采用单纯形法对适应度最优解进行局部寻优．仿真实验表明,该算法比其他优化算法具有更好的性能．     

A local multiobjective optimization algorithm using neighborhood field

A new local search algorithm for multiobjective optimization problems is proposed to find the global optima accurately and diversely. This paper models the cooperatively local search as a potential field, which is called neighborhood field model (NFM). Using NFM, a new Multiobjective Neighborhood Field Optimization (MONFO) algorithm is proposed. In MONFO, the neighborhood field can drive each individual moving towards the superior neighbor and away from the inferior neighbor. MONFO is compared with other popular multiobjective algorithms under twelve test functions. Intensive simulations show that MONFO is able to deliver promising results in the respects of accuracy and diversity, especially for multimodal problems.     

An improved artificial bee colony algorithm based on the strategy of global reconnaissance

The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence optimization algorithm based on the foraging behavior of a honeybee colony. However, many problems are encountered in the ABC algorithm, such as premature convergence and low solution precision. Moreover, it can easily become stuck at local optima. The scout bees start to search for food sources randomly and then they share nectar information with other bees. Thus, this paper proposes a global reconnaissance foraging swarm optimization algorithm that mimics the intelligent foraging behavior of scouts in nature. First, under the new scouting search strategies, the scouts conduct global reconnaissance around the assigned subspace, which is effective to avoid premature convergence and local optima. Second, the scouts guide other bees to search in the neighborhood by applying heuristic information about global reconnaissance. The cooperation between the honeybees will contribute to the improvement of optimization performance and solution precision. Finally, the prediction and selection mechanism is adopted to further modify the search strategies of the employed bees and onlookers. Therefore, the search performance in the neighborhood of the local optimal solution is enhanced. The experimental results conducted on 52 typical test functions show that the proposed algorithm is more effective in avoiding premature convergence and improving solution precision compared with some other ABCs and several state-of-the-art algorithms. Moreover, this algorithm is suitable for optimizing high-dimensional space optimization problems, with very satisfactory outcomes.     

多搜索策略协同进化的人工蜂群算法

针对人工蜂群算法存在的计算精度不高、收敛速度较慢的缺点,提出一种多搜索策略协同进化的人工蜂群算法.所提出的算法在引领蜂和跟随蜂进行邻域搜索时,动态调整搜索的维数以提高搜索效率,并结合人工蜂群算法不同搜索策略的特点,使其协同进化,以平衡算法的局部搜索能力和全局搜索能力.14个基准函数的仿真实验结果表明,所提出的算法能有效改善寻优性能,增强摆脱局部最优的能力.与其他一些改进的人工蜂群算法相比,具有较快的收敛速度和较高的求解精度.     

混合算法的邻域结构变更研究及在排样问题上的应用

本文研究了全局搜索算法和局部搜索算法的混合机制,设计了基于邻域搜索和遗传算法的混合搜索算法。该算法结合了遗传算法的全局搜索特性和邻域局部贪婪搜索特性;在分析排样问题碰靠过程特征的基础上,构建了排样问题邻域假设,当邻域假设满足时,遗传算法+邻域搜索能很好发挥作用;当不能判断邻域结构是否满足邻域假设时,提出了建立遗传算法+匹配变邻域的搜索算法,该算法兼顾了组合优化中邻域搜索的局部搜索无效的情况,实现了匹配的变邻域混合算法在排样优化问题中的应用。实例结果标明,排样图形不一样,其求解难度不一样,该算法均搜索到了更好的排样模式,验证了算法的有效性。     

Packing unit spheres into the smallest sphere using VNS and NLP

This paper addresses the NP hard optimization problem of packing identical spheres of unit radii into the smallest sphere (PSS). It models PSS as a non-linear program (NLP) and approximately solves it using a hybrid heuristic which couples a variable neighborhood search (VNS) with a local search (LS). VNS serves as the diversification mechanism whereas LS acts as the intensification one. VNS investigates the neighborhood of a feasible local minimum u in search for the global minimum, where neighboring solutions are obtained by shaking one or more spheres of u and the size of the neighborhood is varied by changing the number of shaken spheres, the distance and the direction each sphere is moved. LS intensifies the search around a solution u by subjecting its neighbors to a sequential quadratic algorithm with non-monotone line search (as the NLP solver). The computational investigation highlights the role of LS and VNS in identifying (near) global optima, studies their sensitivity to initial solutions, and shows that the proposed hybrid heuristic provides more precise results than existing approaches. Most importantly, it provides computational evidence that the multiple-start strategy of non-linear programming solvers is not sufficient to solve PSS. Finally, it gives new upper bounds for 29 out of 48 benchmark instances of PSS.     

A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems

This paper addresses the flexible job shop scheduling problem (fJSP) with three objectives: min makespan, min maximal machine workload and min total workload. We developed a hybrid genetic algorithm (GA) for the problem. The GA uses two vectors to represent solutions. Advanced crossover and mutation operators are used to adapt to the special chromosome structure and the characteristics of the problem. In order to strengthen the search ability, individuals of GA are first improved by a variable neighborhood descent (VND), which involves two local search procedures: local search of moving one operation and local search of moving two operations. Moving an operation is to delete the operation, find an assignable time interval for it, and allocate it in the assignable interval. We developed an efficient method to find assignable time intervals for the deleted operations based on the concept of earliest and latest event time. The local optima of moving one operation are further improved by moving two operations simultaneously. An extensive computational study on 181 benchmark problems shows the performance of our approach.     

A hybrid particle swarm optimization algorithm for the vehicle routing problem

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for successfully solving one of the most popular supply chain management problems, the vehicle routing problem. The vehicle routing problem is considered one of the most well studied problems in operations research. The proposed algorithm for the solution of the vehicle routing problem, the hybrid particle swarm optimization (HybPSO), combines a particle swarm optimization (PSO) algorithm, the multiple phase neighborhood search–greedy randomized adaptive search procedure (MPNS–GRASP) algorithm, the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy. The algorithm is suitable for solving very large-scale vehicle routing problems as well as other, more difficult combinatorial optimization problems, within short computational time. It is tested on a set of benchmark instances and produced very satisfactory results. The algorithm is ranked in the fifth place among the 39 most known and effective algorithms in the literature and in the first place among all nature inspired methods that have ever been used for this set of instances.     

Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm

Two-sided assembly lines are usually found in the factories which produce large-sized products. In most literatures, the task times are assumed to be deterministic while these tasks may have varying operation times in real application, causing the reduction of performance or even the infeasibility of the schedule. Moreover, the ignorance of some specific constraints including positional constraints, zoning constraints and synchronism constraints will result in the invalidation of the schedule. To solve this stochastic two-sided assembly line balancing problem with multiple constraints, we propose a hybrid teaching-learning-based optimization (HTLBO) approach which combines both a novel teaching-learning-based optimization algorithm for global search and a variable neighborhood search with seven neighborhood operators for local search. Especially, a new priority-based decoding approach is developed to ensure that the selected tasks satisfy most of the constraints identified by multiple thresholds of the priority value and to reduce the idle times related to sequence-dependence among tasks. Experimental results on benchmark problems demonstrate both remarkable efficiency and universality of the developed decoding approach, and the comparison among 11 algorithms shows the effectiveness of the proposed HTLBO.