一种新的量子群进化算法研究

提出了一种基于量子进化的量子群进化算法，使用量子角表示量子比特的状态，并引入改进的粒子群优化策略，对量子群中各量子的量子角进行自适应动态调整．在对0-1背包问题的求解中，表现出很好的性能．     

Artificial intelligence heuristics in solving vehicle routing problems with time window constraints

This paper describes the authors’ research on various heuristics in solving vehicle routing problem with time window constraints (VRPTW) to near optimal solutions. VRPTW is NP-hard problem and best solved to near optimum by heuristics. In the vehicle routing problem, a set of geographically dispersed customers with known demands and predefined time windows are to be served by a fleet of vehicles with limited capacity. The optimized routines for each vehicle are scheduled as to achieve the minimal total cost without violating the capacity and time window constraints. In this paper, we explore different hybridizations of artificial intelligence based techniques including simulated annealing, tabu search and genetic algorithm for better performance in VRPTW. All the implemented hybrid heuristics are applied to solve the Solomon's 56 VRPTW with 100-customer instances, and yield 23 solutions competitive to the best solutions published in literature according to the authors’ best knowledge.     

面向布局优化问题的多量子态量子进化算法及其应

针对演化算法在求解带平衡约束的圆形布局问题上所出现的早熟现象,提出一种有利于保持种群多样性的多量子态量子进化算法,并结合高效的定位定序启发式方法进行求解。为了高效优化布局顺序,在量子进化算法的基础上：引入多量子态编码和基于平均收敛概率的收敛标准以提高求解速度;引入基于禁忌策略和启发信息的观测方法,使其所得到的n进制解为互不相同的整数串,同时保证优先布局质量大、半径大的小圆;引入动态量子进化策略,有效地引导种群向最优个体进化。在定位规则中引入定位概率函数提高解的精度,数值实验结果表明,该算法能够有效求解带平衡约束的圆形布局问题。     

优化网络入侵特征库的量子进化算法

针对网络入侵检测系统中入侵特征库的性能普遍较差的缺点,提出了一种优化网络入侵特征库的改进量子进化算法（IQEA）。采用特征向量表示染色体结构,借鉴小生境协同进化思想初始化种群,以个体的匹配程度设计适应度函数,使用动态更新和“优体交叉”策略进化种群。仿真实验表明,IQEA的寻优能力和收敛速度均优于量子进化算法和进化算法,经IQEA优化后的入侵特征库,检测能力强,并具有较好的自适应性。     

混合量子算法及其在flow shop问题中的应用

量子进化算法（QEA）是目前较为独特的优化算法,它的理论基础是量子计算。算法充分借鉴了量子比特的干涉性、并行性,使得QEA求解组合优化问题具备了可行性。由于在求解排序问题中,算法本身存在收敛慢,没有利用其它未成熟个体等缺陷,将微粒群算法（PSO）及进化计算思想融入QEA中,构成了混合量子算法（HQA）。采用flow shop经典问题对算法进行了测试,结果证明混合算法克服了QEA的缺陷,对于求解排序问题具有一定的普适性。     

A reactive MILP approach to the multidepot heterogeneous fleet vehicle routing problem with time windows

The time‐window‐constrained vehicle routing problem (VRPTW) is a well‐known combinatorial problem. Its goal is to discover the best set of routes for a vehicle fleet in order to service a given number of customers at minimum cost. Vehicle capacity, maximum service time and time‐window constraints must be satisfied. Most proposed VRPTW optimizing approaches intend to discover the best or a near‐optimal solution at once. Improvement methods are old strategies that apply heuristics to insert customers into tours and/or rearrange nodes to obtain better routes. They are performed until no further improvement is achieved. Little research has been focused on model‐based reactive approaches seeking a better solution by exploring a small solution space around the current solution. This work presents a new model‐based improvement methodology for the multi‐depot heterogeneous‐fleet VRPTW problem to enhance an initial solution through solving a series of MILP mathematical problems that allow exchanges of nodes among tours and node reordering on every route. By restricting the range of improvement options, the problem size can be bounded and a limited number of binary variables is required for real‐world problems. The improvement formulation is based on a continuous time‐domain representation that handles assignment and sequencing decisions through different sets of binary variables and uses the notion of a generalized predecessor instead of a direct predecessor. Several types of VRPTW problems have been efficiently solved.     

基于自适应机制的多宇宙并行量子衍生进化算法

进化参量的选取对量子衍生进化算法(QEA)的优化性能有极大的影响,传统QEA在选择进化参量时并未考虑种群中个体间的差异,种群中所有个体采用相同的进化参量完成更新,导致算法在解决组合优化问题中存在收敛速度慢、容易陷入局部最优解等问题。针对这一问题,采用自适应机制调整QEA的旋转角步长和量子变异概率,算法中任意一代的任一个体的进化参量均由该个体自身适应度确定,从而保证尽可能多的进化个体能够朝着最优解方向不断靠近。此外,由于自适应量子进化算法需要评估个体的适应度,导致运算时间较长,针对这一问题则采用多宇宙机制将算法分布于多个宇宙中并行实现,从而提高算法的执行效率。通过搜索多峰函数最优解和求解背包问题测试算法性能,结果表明,与传统QEA相比,所提出算法在收敛速度、搜索全局最优解及执行速度方面具有较好的表现。     

改进量子进化算法及其在物流配送路径优化问题中的应用

量子进化算法的性能直接受量子旋转门旋转角计算方法的影响.文中提出一种改进量子进化算法,核心是设计了基于量子比特概率幅比值自适应计算量子旋转门旋转角的新方法,算法具有收敛速度快和全局搜索能力强的特点.通过0/1背包问题分析了新方法中相关参数对算法性能的影响,并应用算法求解物流配送路径优化问题,仿真表明改进量子进化算法性能优于量子进化算法和传统进化算法.     

一种基于量子进化算法的概率进化算法

针对量子进化算法(QEA)求解二进制编码问题比较有效,而求解多进制编码问题则比较困难,提出一种概率进化算法(PEA)。该算法汲取了量子复合位、叠加态等思想,采用由观测概率构成的概率复合位进行编码,观测和更新操作直接针对观测概率进行。PEA保持了QEA的性能,运算速度远优于QEA,并可以采用任意进制编码。函数优化和背包问题实验验证了PEA的有效性。     

Heuristic methods for vehicle routing problem with time windows

This paper documents our investigation into various heuristic methods to solve the vehicle routing problem with time windows (VRPTW) to near optimal solutions. The objective of the VRPTW is to serve a number of customers within predefined time windows at minimum cost (in terms of distance travelled), without violating the capacity and total trip time constraints for each vehicle. Combinatorial optimisation problems of this kind are non-polynomial-hard (NP-hard) and are best solved by heuristics. The heuristics we are exploring here are mainly third-generation artificial intelligent (AI) algorithms, namely simulated annealing (SA), Tabu search (TS) and genetic algorithm (GA). Based on the original SA theory proposed by Kirkpatrick and the work by Thangiah, we update the cooling scheme and develop a fast and efficient SA heuristic. One of the variants of Glover's TS, strict Tabu, is evaluated and first used for VRPTW, with the help of both recency and frequency measures. Our GA implementation, unlike Thangiah's genetic sectoring heuristic, uses intuitive integer string representation and incorporates several new crossover operations and other advanced techniques such as hybrid hill-climbing and adaptive mutation scheme. We applied each of the heuristics developed to Solomon's 56 VRPTW 100-customer instances, and yielded 18 solutions better than or equivalent to the best solution ever published for these problems. This paper is also among the first to document the implementation of all the three advanced AI methods for VRPTW, together with their comprehensive results.     

视频图像的车辆检测与识别

提出了一种新方法,用来提取视频图像中车辆的候选区域。即将视频图像转换到HSV空间,利用H分量提取图像中红色区域位置,V分量提取图像中车底的水平边缘位置,两者结合确定图像中车辆的候选区域。然后,利用改进的Gabor滤波器组对图像中的候选区域特性进行提取,最后利用支持向量机对提取的候选区域特性进行训练、识别。滤波器组通过量子进化算法进行了改进,其中引入了小生境协同进化算法并对优化后的滤波器组进行聚类减少多余的滤波器,降低冗余度。仿真结果表明此方法提取候选区域更加精确、快速。改进后的量子进化算法收敛速度快,能够快速地找到最优解。     

VNS methods for home care routing and scheduling problem with temporal dependencies,and multiple structures and specialties

The need for optimization in the Home Care Service is becoming more and more legitimate in the face of the increase of demand and cost all over the world. Recently, many researchers in the Operation Research community have been attracted by this issue, which presents interesting aspects related to the vehicle routing problems. In this paper, we consider a new variant called the vehicle routing problem with time windows, temporal dependencies (synchronization, precedence, and disjunction), multi‐structures, and multispecialties problem (VRPTW‐TD‐2MS). This new variant is an extension of the vehicle routing problems with time windows and synchronization constraints (VRPTW‐S) that is well‐studied in literature. We present a Mixed Integer Programming method, and propose three Variable Neighborhood Search approaches. Extensive experiments show the effectiveness and efficiency of the General Variable Neighborhood Search with Ejection Chains‐based local search for solving VRPTW‐TD‐2MS and VRPTW‐S.     

Quantum computation based bundling optimization for combinatorial auction in freight service procurements

Combinatorial auction is a useful trade manner for transportation service procurements in e-marketplaces. To enhance the competition of combinatorial auction, a novel auction mechanism of two-round bidding with bundling optimization is proposed. As the recommended the auction mechanism, the shipper/auctioneer integrates the objects into several bundles based on the bidding results of first round auction. Then, carriers/bidders bid for the object bundles in second round. The bundling optimization is described as a multi-objective model with two criteria on price complementation and combination consistency. A Quantum Evolutionary Algorithm (QEA) with β-based rotation gate and the encoding scheme based on non-zero elements in complementary coefficient matrix is developed for the model solution. Comparing with a Contrast Genetic Algorithm, QEA can achieve better computational performances for small and middle size problems.     

基于MapReduce模型的并行量子进化算法

利用MapReduce模型可自动编写串行程序及编程接口简单的优点,实现量子进化算法在MapReduce模型下的并行化,提出基于MapReduce模型的并行量子进化算法MRQEA,并将其部署到Hadoop云计算平台上运行。对0-1背包问题的测试结果证明,MRQEA算法在处理大型数据集时具有良好的加速比和并行效率。     

Improving the performance of OCL constraint solving with novel heuristics for logical operations: a search-based approach

A common practice to specify constraints on the Unified Modeling Language (UML) models is using the Object Constraint Language (OCL). Such constraints serve various purposes, ranging from simply providing precise meaning to the models to supporting complex verification and validation activities. In many applications, these constraints have to be solved to obtain values satisfying the constraints, for example, in the case of model-based testing (MBT) to generate test data for the purpose of generating executable test cases. In our previous work, we proposed novel heuristics for various OCL constructs to efficiently solve them using search algorithms. These heuristics are enhanced in this paper to further improve the performance of OCL constraint solving. We performed an empirical evaluation comprising of three case studies using three search algorithms: Alternating Variable Method (AVM), (1?+?1) Evolutionary Algorithm (EA), and a Genetic Algorithm (GA) and in addition Random Search (RS) was used as a comparison baseline. In the first case study, we evaluated each heuristics using carefully designed artificial problems. In the second case study, we evaluated the heuristics on various constraints of Cisco’s Video Conferencing Systems defined to support MBT. Finally, the third case study is about EU-Rent Car Rental specification and is obtained from the literature. The results of the empirical evaluation showed that (1?+?1) EA and AVM with the improved heuristics significantly outperform the rest of the algorithms.     

Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm

This paper presents a new model and solution for multi-objective vehicle routing problem with time windows (VRPTW) using goal programming and genetic algorithm that in which decision maker specifies optimistic aspiration levels to the objectives and deviations from those aspirations are minimized. VRPTW involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper uses a direct interpretation of the VRPTW as a multi-objective problem where both the total required fleet size and total traveling distance are minimized while capacity and time windows constraints are secured. The present work aims at using a goal programming approach for the formulation of the problem and an adapted efficient genetic algorithm to solve it. In the genetic algorithm various heuristics incorporate local exploitation in the evolutionary search and the concept of Pareto optimality for the multi-objective optimization. Moreover part of initial population is initialized randomly and part is initialized using Push Forward Insertion Heuristic and λ-interchange mechanism. The algorithm is applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances. Results show that the suggested approach is quiet effective, as it provides solutions that are competitive with the best known in the literature.     

Shipment partitioning and routing to minimize makespan in a transportation network

This paper addresses the problem of partitioning and transporting a shipment of known size through an n-node public transportation network with known scheduled departure and arrival times and expected available capacities for each departure. The objective is to minimize the makespan of shipping. The problem while practical in its scope, has received very little attention in the literature perhaps because of the concentration of research in vehicle routing without regard to partitioning and partitioning without regard to routing. A general non-linear programming model is developed. The model is then converted into a linear model through the Routing First and Assignment Second approach. This approach is different from the general decomposition approaches since they normally do not guarantee optimality. However, the linear model still involves a large number of constraints, and solution is not attempted here. Instead, three heuristics are proposed for solving the problem. Two of the heuristics use iterative techniques to evaluate all possible paths. The third heuristic uses a max-flows approach based upon aggregated capacities to reduce the size of the network presented to the other heuristics. This allows for a good starting point for other heuristics, and may impact the total computational effort. We find that the heuristics developed perform well because in the case of networks that are not congested, they find the optimal solution.     

Vehicle routing with pick-up and delivery: tour-partitioning heuristics

Many applications of the classical vehicle routing problem involve pick-up and delivery services between the depot and peripheral locations (warehouses, stores, stations). This paper studies an important version of the vehicle routing problem with pick-up and delivery (the so-called delivery and backhaul problem): delivery in our case refers to transportation of goods from the depot to customers, and pick-up (backhaul) refers to shipment from customers to the depot. The objective is to find a set of vehicle routes that service customers such that vehicle capacity is not violated and the total distance traveled is minimized. Tour partitioning heuristics for solving the capacitated vehicle routing problem are based on breaking a basic tour into disjoint segments served by different vehicles. This idea is adapted for solving the delivery and backhaul problem. Two heuristics that focus on efficient utilization of vehicles’ capacities are introduced, analyzed and tested numerically.     

Modeling rolling batch planning as vehicle routing problem with time windows

This paper considers the rolling batch planning problem of grouping and sequencing a given set of slabs into several rolling units in iron and steel industry. The existing mathematical methods often used for the problem are traveling salesman problem (TSP) and vehicle routing problem (VRP), but these methods are not precise, because the position limitation of some slabs in a rolling unit scheduling is not considered. Therefore we suggest a new model, vehicle routing problem with time window (VRPTW) to describe the rolling batch planning problem, in which the position limitation of slabs are quantified as the time constraints. Several solution methods including the genetic algorithm are presented for solving the problem and the computational results show that the genetic algorithm is superior to other methods.In this paper, the vehicle routing problem with time window (VRPTW) of combinational optimization is used to analyze and model the rolling batch planning problem. Genetic algorithm and heuristic are used to solve the problem. Simulation results based on the actual production data show that this model is precise and the genetic algorithm based method is very promising.     

一种新的求解TSP的混合量子进化算法

在分析量子进化基本概念的基础上,提出了一种新的求解TSP的混合量子进化算法（MQEA）。该算法将三段优化局部搜索算法融入量子进化机制,采用一种基于边的编码方法,应用最近邻规则设置初始参数,并设计了排序交叉算子以扩展种群的搜索范围。通过选取国际通用旅行商问题（TSP）实例库(TSPLIB)中的多个实例进行测试,表明新算法具有高的精确度和鲁棒性,即使对于中大规模问题（城市数大于500）,也能以很小的种群和微小的相对误差求得满意解。