深层加速搜索的蜂群算法

蜂群(ABC)算法是近年来提出的一种求解优化问题的较新型的仿生进化算法。针对蜂群算法的不足,依据反向搜索的思想,提出一种改进的蜂群算法。在改进算法中,每次邻域搜索之后,通过比较新旧食物源位置的花蜜值(而非适应度)来选择保留较优解。同时,在采蜜蜂采蜜后以一定概率进行反向搜索,保留较优解。邻域搜索的维数也不再限定某一维。基于五个标准测试函数的仿真结果表明,本算法能有效加快收敛速度,提高最优解的精度,其性能明显优于基本的蜂群算法。     

变邻域搜索算法综述

变邻域搜索算法(Variable Neighborhood Search,VNS)作为一种新的元启发式算法,已初步成功地用于解决优化问题,尤其是对于大规模组合优化问题效果良好。对VNS的扩展研究层出不穷,并将其成功地应用到旅行商问题、车辆路径问题、调度、图着色等问题中。简述了经典的元启发式算法,并依次论述了优化问题,VNS算法起源,VNS算法原理,VNS算法分析,扩展的VNS分析,VNS在初始解构造、邻域结构构造、局部搜索和停止准则几个方面的改进方法,针对不同版本的VNS归纳了其在各种优化问题应用情况。基于对改进的VNS的分类,从算法自身研究角度和实际应用角度提出了未来研究方向。     

校车路径问题的改进迭代局部搜索算法

针对考虑站点服务时间、学生最大乘车时间约束的校车路径问题(SBRP),提出一种改进迭代局部搜索(ILS)算法以提升求解质量。该算法使用大规模邻域搜索(LNS)算法作为扰动算子;在解的破坏过程中,设计一组解的破坏因子并赋以一定的选择概率,每隔若干次迭代后根据解的质量自适应更改破坏因子的选择概率,进而调整解的破坏程度。为提升ILS解的多样性,算法采用了基于偏差系数的邻域解接受准则。在国际基准测试案例上进行了测试,测试结果表明在ILS算法中使用自适应调整破坏程度的LNS扰动比常规扰动和其他破坏扰动的求解质量有大幅提升;与蚁群算法的比较结果进一步验证了改进算法的有效性。     

求解多车型校车路径问题的带参数选择机制的GRASP算法

考虑到校车路径安排过程中不同车型容量和成本的差异,建立了多车型校车路径问题(SBRP)模型,并提出了一种带参数选择机制的贪婪随机自适应(GRASP)算法进行求解。在初始解构造阶段,设计一组阈值参数控制受限候选列表(RCL)的大小,使用轮盘赌法选择阈值参数。完成初始解构造后,使用可变邻域搜索(VNS)进行邻域解改进,并记录所选择的参数和解的目标值。算法迭代过程中,先设置相同阈值参数的选择概率,每隔若干次迭代后,评估每个阈值参数的性能并修改其选择概率,使得算法能够得到更好的平均解。使用基准测试案例进行了测试,比较了基本GRASP算法与设计的GRASP算法的性能,并与现有求解多车型校车路径问题的算法进行对比,实验结果表明所设计的算法是有效的。     

面向自动组卷问题的改进类电磁算法

类电磁算法（EM）中局部搜索是按一定步长进行线性搜索,在这个范围内寻找个体在某一维上的最优值。由于步长的限定,求得的该维上最优值可能远离实际的最优值。采用遗传算法（GA）中选择因子和交叉因子可以很好地解决这一问题。在组卷系统中,通过基于遗传算法改进的类电磁算法（Based Genetic Electromagnetism-like Mechanism Algorithm,GEM）与GA算法以及采用线性局部搜索的EM算法实验的比较,证明该算法有更高的组卷效率。     

面向柔性作业车间调度问题的改进变邻域搜索算法

针对柔性作业车间调度问题的特点,提出一种求解该问题的改进变邻域搜索算法。结合问题特点设计合理的编码方式,采用遗传算法进行最优解搜索,将搜索的结果作为变邻域搜索算法的初始解,以提高初始解的质量。为提高局部搜索能力,设计3种不同的邻域结构,构建邻域结构集以产生邻域解,保证邻域解的搜索过程中解的可行性以提高求解效率。针对一系列典型的柔性作业车间调度问题的实例,运用所设计的改进变邻域搜索算法进行测试求解,并将计算结果与文献中其他算法的测试结果进行比较,验证了所提出方法求解柔性作业车间调度问题的可行性和有效性。     

基于跳跃辅助工作策略的混流装配线排产优化

为了使混流装配线高效运作, 研究了一类基于跳跃辅助工作策略的混流装配线排产优化问题. 以同时优化空闲费用和辅助工作费用为目标, 建立了一类基于跳跃辅助工作策略的混流装配线排产优化模型, 给出了执行跳跃辅助工作策略的一个必要条件和辅助工作费用的一个下界. 然后证明了该类优化问题是强NP难的, 由于该问题的强NP难性, 提出了一种嵌入式变邻域类电磁机制(Variable neighborhood search-electromagnetism-like mechanism, VNS-EM)混合算法求解该模型, 为了避免算法陷入局部最优, 在类电磁机制算法的每次迭代过程中嵌入改进的变邻域搜索算法, 利用变邻域搜索算法较好的局部搜索能力对最好个体的邻域进行精细搜索, 从而提高了解的质量. 仿真结果验证了该方法的可行性和有效性.     

基于分段混合蛙跳算法的旅行商问题求解

针对旅行商问题(TSP)在搜索后期解的多样性和精度下降的问题,提出一种解决TSP问题的分段混合蛙跳算法(S-SFLA)。该算法在搜索初期利用逆转变异算子减少交叉路径,在搜索的后期引入邻域搜索(个体邻域,局部最优领域,全局最优邻域)增加种群多样性。在整个搜索过程中记忆全局历史最优解与局部历史最优解,进行全局更新和局部更新,避免迂回搜索。在局部更新中,每一个青蛙都有机会得到更新。实验结果表明,与遗传算法、蚁群算法、基本蛙跳算法相比,S-SFLA算法在求解中等规模的TSP问题上具有更快的搜索速度和更高的求解精度。     

求解柔性作业车间调度问题的新型改进Jaya算法

为有效解决复杂的柔性作业车间调度问题,以最小化最大完成时间为目标,提出了一种结合了变邻域搜索算法的新型改进Jaya算法来求解。为不断挖掘和优化探索最优解,提高算法求解的结果质量,通过Jaya算法的原理重新提出一种解的更新机制,此外在Jaya算法原理的基础上嵌入一种变邻域搜索策略,并在传统邻域结构的基础上重新设计了两种新型邻域结构,扩大了邻域搜索范围,增强了Jaya算法的局部搜索能力,避免算法因失去解的多样性从而陷入局部最优。运用基准算例对该算法的求解性能进行了验证,并与其他算法的仿真结果进行对比,结果表明该改进算法的求解效率更高。     

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。     

结合变邻域搜索的竞争Hopfield神经网络解决最大分散度问题

提出一种结合变邻域搜索的离散竞争Hopfield神经网络,用于求解最大分散度问题。为了克服神经网络易陷入局部最小值的问题,将变邻域搜索的思想引入到离散竞争Hopfield神经网络中,一旦网络陷入局部最小值,变邻域搜索能帮助神经网络动态改变搜索邻域,从而跳出局部最小值去搜寻更优的解。最后,针对最大分散度问题的实验结果表明,提出的算法具有良好的性能。     

A variable neighborhood search based approach for uncapacitated multilevel lot-sizing problems

In this paper, an effective approach based on the variable neighborhood search (VNS) algorithm is presented to solve the uncapacitated multilevel lot-sizing (MLLS) problems with component commonality and multiple end-items. A neighborhood structure for the MLLS problem is defined, and two kinds of solution move policies, i.e., move at first improvement (MAFI) and move at best improvement (MABI), are used in the algorithm. A new rule called Setup shifting is developed to conduct a more efficient neighborhood search for the MLLS problems. Computational studies are carried out on two sets of benchmark problems. The experimental results show that the VNS algorithm is capable of solving MLLS problems with good optimality and high computational efficiency as well, outperforming most of the existing algorithms in comparison.     

Variable neighborhood search based approaches to a vehicle scheduling problem in agriculture

A vehicle scheduling problem (VSP) that arises from sugar beet transportation within minimum working time under the set of constraints reflecting a real‐life situation is considered. A mixed integer quadratically constrained programming (MIQCP) model of the considered VSP and reformulation to a mixed integer linear program (MILP) are proposed and used within the framework of Lingo 17 solver, producing optimal solutions only for small‐sized problem instances. Two variants of the variable neighborhood search (VNS) metaheuristic—basic VNS (BVNS) and skewed VNS (SVNS) are designed to efficiently deal with large‐sized problem instances. The proposed VNS approaches are evaluated and compared against Lingo 17 and each other on the set of real‐life and generated problem instances. Computational results show that both BVNS and SVNS reach all known optimal solutions on small‐sized instances and are comparable on medium‐ and large‐sized instances. In general, SVNS significantly outperforms BVNS in terms of running times.     

Optimizing frequent time-window selection for association rules mining in a temporal database using a variable neighbourhood search

In this study, we investigate the problem of maximum frequent time-window selection (MFTWS) that appears in the process of discovering association rules time-windows (ARTW). We formulate the problem as a mathematical model using integer programming that is a typical combination problem with a solution space exponentially related to the problem size. A variable neighbourhood search (VNS) algorithm is developed to solve the problem with near-optimal solutions. Computational experiments are performed to test the VNS algorithm against a benchmark problem set. The results show that the VNS algorithm is an effective approach for solving the MTFWS problem, capable of discovering many large-one frequent itemset with time-windows (FITW) with a larger time-coverage rate than the lower bounds, thus laying a good foundation for mining ARTW.     

极小化时间表长的无等待流水车间调度

针对以时间表长最小为目标函数的无等待流水车间（No-Wait Flow Shop,NWFS）调度问题,提出了一个混合禁忌搜索算法（Hybrid Taboo Search,HTS）,以启发式算法产生的解作为初始解,通过禁忌搜索进一步提高解的质量。大量随机产生实例的实验结果表明：提出的HTS算法在总体性能上优于经典的RAJ、VNS和GASA算法,因此该算法具有可行性和优越性。     

A GRASP with evolutionary path relinking for the truck and trailer routing problem

In the truck and trailer routing problem (TTRP) a heterogeneous fleet composed of trucks and trailers has to serve a set of customers, some only accessible by truck and others accessible with a truck pulling a trailer. This problem is solved using a route-first, cluster-second procedure embedded within a hybrid metaheuristic based on a greedy randomized adaptive search procedure (GRASP), a variable neighborhood search (VNS) and a path relinking (PR). We test PR as a post-optimization procedure, as an intensification mechanism, and within evolutionary path relinking (EvPR). Numerical experiments show that all the variants of the proposed GRASP with path relinking outperform all previously published methods. Remarkably, GRASP with EvPR obtains average gaps to best-known solutions of less than 1% and provides several new best solutions.     

A parallel variable neighborhood search for the vehicle routing problem with divisible deliveries and pickups

A well-known variant of the vehicle routing problem involves backhauls, where vehicles deliver goods from a depot to linehaul customers and pick up goods from backhaul customers to the depot. The vehicle routing problem with divisible deliveries and pickups (VRPDDP) allows vehicles to visit each client once or twice for deliveries or pickups. In this study, a very efficient parallel approach based on variable neighborhood search (VNS) is proposed to solve VRPDDP. In this approach, asynchronous cooperation with a centralized information exchange strategy is used for parallelization of the VNS approach, called cooperative VNS (CVNS). All available problem sets of VRPDDP have been successfully solved with the CVNS, and the best solutions available in the literature have been significantly improved.     

Variable neighborhood search for the inventory routing and scheduling problem in a supply chain

The inventory, routing and scheduling decisions are three major driving factors for supply chain performance. Since they are related to one another in a supply chain, they should be determined simultaneously to improve the decision quality. In the past, the inventory policy, vehicle routing and vehicle scheduling are determined sequentially and separately. Hence, the total cost (inventory, routing and vehicle costs) would increase. In this paper, an integrated model for the inventory routing and scheduling problem (IRSP) is proposed. Since searching for the optimal solution for this model is a non-polynomial (NP) problem, a metaheuristic, variable neighborhood search (VNS), is proposed. The proposed method was compared with other existing methods. The experimental results indicate that the proposed method is better than other methods in terms of average cost per day.     

Variable neighborhood search for two-agent flow shop scheduling problem

Multi-agent scheduling in flow shop environment is seldom considered. In this paper flow shop scheduling problem with two agents is studied and its feasibility model is considered, in which the goal is to minimize the makespan of the first agent and the total tardiness of the second agent simultaneously under the given upper bounds. A simple variable neighborhood search (VNS) algorithm is proposed, in which a learning neighborhood structure is constructed to produce new solutions and a new principle is applied to decide if the current solution can be replaced with the new one. VNS is tested on a number of instances and the computational results show the promising advantage of VNS when compared to other algorithms of the problem.