A cooperative parallel metaheuristic for the capacitated vehicle routing problem

This paper introduces a cooperative parallel metaheuristic for the capacitated vehicle routing problem. The proposed metaheuristic consists of multiple parallel tabu search threads that cooperate by asynchronously exchanging best-found solutions through a common solution pool. The solutions sent to the pool are clustered according to their similarities. The search history information identified from the solution clusters is applied to guide the intensification or diversification of the tabu search threads. Computational experiments on two sets of large-scale benchmark instance sets from the literature demonstrate that the suggested metaheuristic is highly competitive, providing new best solutions to ten of those well-studied instances.     

一种求解最大团问题的并行交叉熵算法

为了提高交叉熵算法求解最大团问题(maximum clique problem,MCP)的性能,提出一种领导者-跟随者协作求解的并行策略来实现交叉熵算法,从而达到减少计算时间和保障解的质量这两方面的平衡.算法中领导者活跃在并行处理器之间采集数据,并根据当前获得信息对跟随者作出决策;受控的跟随者则主要根据领导者的决策信息自适应地调整搜索空间,完成各自的集团产生任务.采用了OpenMPI在MIMD平台上实现了该算法,并应用到MCP基准测试问题上.加速比和效率分析结果表明,算法具有很好的加速比和效率.而与其它几种当前最好的启发式算法相比,结果表明算法相对于基于种群的启发式算法有一定的性能改善.     

并行机间歇过程生产调度的遗传局部搜索算法

研究了一类集成分批的并行机间歇过程调度问题(parallel machine batch process scheduling problem,简称PBPSP),将此问题转化为固定费用运输问题(6xed charge transportation problem,简称FCTP)后,提出了具有集中邻域搜索机制和局部最优逃逸机制的遗传局部搜索算法(genetic local search algorithm,简称GLSA).GLSA算法用先根遍历边排列模式编码生成树解,具有高效的子树补充式单点交叉操作.将基于网络单纯型方法的邻域搜索作为变异算子,并提出了连续随机节点邻域搜索的集中邻域搜索策略以及随机旋转变异与全局邻域搜索相结合的局部最优逃逸策略,极大地强化了遗传局部搜索算法的全局寻优能力.实验表明:GLSA算法获得的解质量优于基于排列编码的遗传算法和基于矩阵编码的遗传算法,得到了所有Benchmark问题的最优解,且具有高鲁棒性.针对一定规模的FCTP问题,GLSA算法比Tabu启发式搜索算法具有更高的获得最优解几率.     

Metaheuristics for the single machine weighted quadratic tardiness scheduling problem

This paper considers the single machine scheduling problem with weighted quadratic tardiness costs. Three metaheuristics are presented, namely iterated local search, variable greedy and steady-state genetic algorithm procedures. These address a gap in the existing literature, which includes branch-and-bound algorithms (which can provide optimal solutions for small problems only) and dispatching rules (which are efficient and capable of providing adequate solutions for even quite large instances). A simple local search procedure which incorporates problem specific information is also proposed.The computational results show that the proposed metaheuristics clearly outperform the best of the existing procedures. Also, they provide an optimal solution for all (or nearly all, in the case of the variable greedy heuristic) the smaller size problems. The metaheuristics are quite close in what regards solution quality. Nevertheless, the iterated local search method provides the best solution, though at the expense of additional computational time. The exact opposite is true for the variable greedy procedure, while the genetic algorithm is a good all-around performer.     

求解最大割问题的多启动禁忌搜索算法

为了增强局部搜索算法在求解最大割问题上的寻优能力,提高解质量,提出了一种多启动禁忌搜索（MSTS）算法。算法主要包括两个重要组件:一是用于搜索高质量局部优化解的禁忌搜索算法;二是具有全局搜索能力的重启策略。算法首先通过禁忌搜索组件获取局部优化解;然后应用设计的重启策略重新生成初始解并重启禁忌搜索过程。重启策略基于随机贪心的思想,综合利用了“构造”和“扰动”这两种方法生成新的起始解,来逃离局部最优的陷阱从而找到更高优度的解。采用了国际文献中公认的21个算例作为本算法的测试实验集并进行实算, 并与多个先进算法进行比较,MSTS算法在18个算例上得到最好解值,高于其他对比算法。实验结果表明,MSTS算法具有更强的寻优能力和更高的解质量。     

A guided cooperative search for the vehicle routing problem with time windows

We have developed a pattern-identification mechanism that endows cooperative search with capabilities to create new information and guide the global search. The proposed mechanism sends information to independent metaheuristics about promising and unpromising patterns in the solution space. By fixing or prohibiting specific solution attribute values in certain search metaheuristics, we can focus the search on desired regions. The mechanism thus enforces better coordination between individual methods and controls the global search's diversification and intensification. An enhanced cooperative-search mechanism creates new information from exchanged solutions and guides the global search with a pattern-identification mechanism.     

Parallel multisource view maintenance

In a distributed environment, materialized views are used to integrate data from different information sources and then store them in some centralized location. In order to maintain such materialized views, maintenance queries need to be sent to information sources by the data warehouse management system. Due to the independence of the information sources and the data warehouse, concurrency issues are raised between the maintenance queries and the local update transactions at each information source. Recent solutions such as ECA and Strobe tackle such concurrent maintenance, however with the requirement of quiescence of the information sources. SWEEP and POSSE overcome this limitation by decomposing the global maintenance query into smaller subqueries to be sent to every information source and then performing conflict correction locally at the data warehouse. Note that all these previous approaches handle the data updates one at a time. Hence either some of the information sources or the data warehouse is likely to be idle during most of the maintenance process. In this paper, we propose that a set of updates should be maintained in parallel by several concurrent maintenance processes so that both the information sources as well as the warehouse would be utilized more fully throughout the maintenance process. This parallelism should then improve the overall maintenance performance. For this we have developed a parallel view maintenance algorithm, called PVM, that substantially improves upon the performance of previous maintenance approaches by handling a set of data updates at the same time. The parallel handling of a set of updates is orthogonal to the particular maintenance algorithm applied to the handling of each individual update. In order to perform parallel view maintenance, we have identified two critical issues that must be overcome: (1) detecting maintenance-concurrent data updates in a parallel mode and (2) correcting the problem that the data warehouse commit order may not correspond to the data warehouse update processing order due to parallel maintenance handling. In this work, we provide solutions to both issues. For the former, we insert a middle-layer timestamp assignment module for detecting maintenance-concurrent data updates without requiring any global clock synchronization. For the latter, we introduce the negative counter concept to solve the problem of variant orders of committing effects of data updates to the data warehouse. We provide a proof of the correctness of PVM that guarantees that our strategy indeed generates the correct final data warehouse state. We have implemented both SWEEP and PVM in our EVE data warehousing system. Our performance study demonstrates that a manyfold performance improvement is achieved by PVM over SWEEP.Received: 12 November 2001, Accepted: 18 December 2002, Published online: 31 July 2003This work was supported in part by the NSF NYI grant IIS-979624 and NSF CISE Instrumentation grant IRIS 97-29878 and NSF grant IIS-9988776.     

基于记忆库粒子群算法的海上协作搜寻计划制定

海上搜寻任务通常由多个设施协作完成。针对海上协作搜寻计划制定问题,提出一种记忆库粒子群算法。该算法利用组合优化策略和连续优化策略,首先为单个设施生成相应的备选解并构建记忆库,通过从记忆库中学习、随机生成两种方式生成新的备选解;然后采用网格法更新记忆库,每个网格中最多有一个备选解保存在记忆库中,保证记忆库中备选解的多样性,基于此对解空间进行有效的全局搜索;最后通过从记忆库中随机选择多个备选解组合生成初始协作搜寻方案,利用粒子群策略围绕质量较好的备选解进行有效的局部搜索。实验结果表明,在效率方面,所提算法运行时间较短,在获取最小方差的同时可提高1%~5%的任务成功率,可有效应用于海上协作搜寻计划制定。     

基于交叉变异策略的双种群差分进化算法

为加强差分进化算法的全局搜索能力,提出了一种基于交叉变异策略的双种群差分进化算法（CMDPDE）。CMDPDE中,两个种群分别采用大小不同的缩放因子和交叉因子,在每代进化完毕后,对其中缩放因子和交叉因子较小的种群执行交叉或变异策略来寻找更优的个体,同时两个种群之间每10代进行一次信息交流。这种方式与单种群差分进化算法相比,可以通过双种群和交叉变异策略来增加解的多样性,使算法能在更大的范围内寻优。6个Benchmark函数的实验结果证明CMDPDE具有较好的寻优能力。     

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.     

A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery

When solving a wide range of complex scenarios of a given optimization problem, it is very difficult, if not impossible, to develop a single technique or algorithm that is able to solve all of them adequately. In this case, it is necessary to combine several algorithms by applying the most appropriate one in each case. Parallel computing can be used to improve the quality of the solutions obtained in a cooperative algorithms model. Exchanging information between parallel cooperative algorithms will alter their behavior in terms of solution searching, and it may be more effective than a sequential metaheuristic. For demonstrating this, a parallel cooperative team of four multiobjective evolutionary algorithms based on OpenMP is proposed for solving different scenarios of the Motif Discovery Problem (MDP), which is an important real-world problem in the biological domain. As we will see, the results show that the application of a properly configured parallel cooperative team achieves high quality solutions when solving the addressed problem, improving those achieved by the algorithms executed independently for a much longer time.     

Intelligent scheduling with tabu search: An application to jobs with linear delay penalties and sequence-dependent setup costs and times

In this article we study thetabu search (TS) method in an application for solving an important class of scheduling problems. Tabu search is characterized by integrating artificial intelligence and optimization principles, with particular emphasis on exploiting flexible memory structures, to yield a highly effective solution procedure. We first discuss the problem of minimizing the sum of the setup costs and linear delay penalties when N jobs, arriving at time zero, are to be scheduled for sequential processing on a continuously available machine. A prototype TS method is developed for this problem using the common approach of exchanging the position of two jobs to transform one schedule into another. A more powerful method is then developed that employs insert moves in combination with swap moves to search the solution space. This method and the best parameters found for it during the preliminary experimentation with the prototype procedure are used to obtain solutions to a more complex problem that considers setup times in addition to setup costs. In this case, our procedure succeeded in finding optimal solutions to all problems for which these solutions are known and a better solution to a larger problem for which optimizing procedures exceeded a specified time limit (branch and bound) or reached a memory overflow (branch and bound/dynamic programming) before normal termination. These experiments confirm not only the effectiveness but also the robustness of the TS method, in terms of the solution quality obtained with a common set of parameter choices for two related but different problems.     

Multi-strategy ensemble grey wolf optimizer and its application to feature selection

To overcome the limitation of single search strategy of grey wolf optimizer (GWO) in solving various function optimization problems, we propose a multi-strategy ensemble GWO (MEGWO) in this paper. The proposed MEGWO incorporates three different search strategies to update the solutions. Firstly, the enhanced global-best lead strategy can improve the local search ability of GWO by fully exploiting the search space around the current best solution. Secondly, the adaptable cooperative strategy embeds one-dimensional update operation into the framework of GWO to provide a higher population diversity and promote the global search ability. Thirdly, the disperse foraging strategy forces a part of search agents to explore a promising area based on a self-adjusting parameter, which contributes to the balance between the exploitation and exploration. We conducted numerical experiments based on various functions form CEC2014. The obtained results are compared with other three modified GWO and seven state-of-the-art algorithms. Furthermore, feature selection is employed to investigate the effectiveness of MEGWO on real-world applications. The experimental results show that the proposed algorithm which integrate multiple improved search strategies, outperforms other variants of GWO and other algorithms in terms of accuracy and convergence speed. It is validated that MEGWO is an efficient and reliable algorithm not only for optimization of functions with different characteristics but also for real-world optimization problems.     

Nearest neighbour cuckoo search algorithm with probabilistic mutation

In this study, we present a nearest neighbour cuckoo search algorithm with probabilistic mutation, called NNCS. In the proposed approach, the nearest neighbour strategy is utilized to select guides to search for new solutions by using the nearest neighbour solutions instead of the best solution obtained so far. In the proposed strategy, we respectively employ a solution-based and a fitness-based similar metrics to select the nearest neighbour solutions for implementation. Furthermore, the probabilistic mutation strategy is used to control the new solutions learn from the nearest neighbour ones in partial dimensions only. In addition, the nearest neighbour strategy helps the best solution participate in searching too. Extensive experiments, which are carried on 20 benchmark functions with different properties, demonstrate the improvement in effectiveness and efficiency of the nearest neighbour strategy and the probabilistic mutation strategy.     

Using tabu search with ranking candidate list to solve production planning problems with setups

This study considers production planning problems involving multiple products, multiple resources, multiple periods, setup times, and setup costs. It can be formulated as a mixed integer program (MIP). Solving a realistic MIP production planning problem is NP-hard; therefore, we use tabu search methods to solve such a difficult problem. Furthermore, we improve tabu search by a new candidate list strategy, which sorts the neighbor solutions using post-optimization information provided by the final tableau of the linear programming simplex algorithm. A neighbor solution with higher priority in the ranking sequence has a higher probability of being the best neighbor solution of a current solution. According to our experiments, the proposed candidate list strategy tabu search produces a good solution faster than the traditional simple tabu search. This study also suggests that if the evaluation of the entire neighborhood space in a tabu search algorithm takes too much computation and if an efficient and effective heuristic to rank the neighbor solutions can be developed, the speed of tabu search algorithm could be significantly increased by using the proposed candidate list strategy.     

多目标混合遗传算法求解流水车间调度问题

为高效地求解多目标流水车间调度问题,提出了一种多目标混合遗传算法,此算法将局部搜索融入进化计算中,采用非劣解并行局部搜索策略,并依据基于Pareto支配关系的个体排序数和密度值进行适应度赋值,以加速算法的收敛,保持群体多样性.仿真结果表明,新算法能够有效地解决多目标流水车间调度问题.     

An effective asexual genetic algorithm for solving the job shop scheduling problem

By using the notion of elite pool, this paper presents an effective asexual genetic algorithm for solving the job shop scheduling problem. Based on mutation operations, the algorithm selectively picks the solution with the highest quality from the pool and after its modification, it can replace the solution with the lowest quality with such a modified solution. The elite pool is initially filled with a number of non-delay schedules, and then, in each iteration, the best solution of the elite pool is removed and mutated in a biased fashion through running a limited tabu search procedure. A decision strategy which balances exploitation versus exploration determines (i) whether any intermediate solution along the run of tabu search should join the elite pool, and (ii) whether upon joining a new solution to the pool, the worst solution should leave the pool. The genetic algorithm procedure is repeated until either a time limit is reached or the elite pool becomes empty. The results of extensive computational experiments on the benchmark instances indicate that the success of the procedure significantly depends on the employed mechanism of updating the elite pool. In these experiments, the optimal value of the well-known 10 × 10 instance, ft10, is obtained in 0.06 s. Moreover, for larger problems, solutions with the precision of less than one percent from the best known solutions are achieved within several seconds.     

求解混合流水车间调度问题的离散布谷鸟算法

为求解混合流水车间调度问题,提出一种离散布谷鸟算法。针对常规解码方法难以获得最优解的缺点,提出一种改进的解码方法,基于工件数与并行机数,按概率随机分配机器;根据标准布谷鸟算法中莱维飞行和巢寄生行为两种位置更新策略的核心思想,提出基于位置交叉和个体距离的离散莱维飞行,设计基于最优插入和最优交换的巢寄生策略。最后算例对比实验结果显示,采用基于改进解码方法的离散布谷鸟算法求解所得结果的平均值最小,验证了改进解码方法能提高解的质量;实例测试所得结果均获得了当前最优解,验证了离散布谷鸟算法求解该类问题的优越性。     

Parallel ray tracing based upon a multilevel topological knowledge acquisition of the scene

Including the standard parallelization by grouping primary rays, this paper presents a new parallel ray-timing method based upon a topological knowledge acquisition of the scene. This topological knowledge focuses on relative positions between objects and processes and uses a new type of message. Indeed, instead of exchanging database pages or rays, processes exchange topological information. This information is used by each process to decrease its own list of objects to test against rays The acquisition of information about relative positions between objects and processes is obtained by a careful ordering of he pixel calculation. The processes are dispatched on a computer network including a parallel computer The organization of the processes on this network is a multilevel one leading to different levels of topological message exchanges This method is characterized by topological messages describing the scene, dynamic optimization of the database, easy parallelization on any network (no deadlock, fault tolerance, easily expandable and simple routing), and gives interesting results with true or simulated parallelism.     

Multi-agent team cooperation: A game theory approach

The main goal of this work is to design a team of agents that can accomplish consensus over a common value for the agents’ output using cooperative game theory approach. A semi-decentralized optimal control strategy that was recently introduced by the authors is utilized that is based on minimization of individual cost using local information. Cooperative game theory is then used to ensure team cooperation by considering a combination of individual cost as a team cost function. Minimization of this cost function results in a set of Pareto-efficient solutions. Among the Pareto-efficient solutions the Nash-bargaining solution is chosen. The Nash-bargaining solution is obtained by maximizing the product of the difference between the costs achieved through the optimal control strategy and the one obtained through the Pareto-efficient solution. The latter solution results in a lower cost for each agent at the expense of requiring full information set. To avoid this drawback some constraints are added to the structure of the controller that is suggested for the entire team using the linear matrix inequality (LMI) formulation of the minimization problem. Consequently, although the controller is designed to minimize a unique team cost function, it only uses the available information set for each agent. A comparison between the average cost that is obtained by using the above two methods is conducted to illustrate the performance capabilities of our proposed solutions.