高超声速飞行器水平航迹规划算法研究

伴随高超声速飞行器的出现,防空系统也变得越来越复杂,为了提高高超声速飞行器在空战中的生存概率,有必要对多约束远程轨迹优化技术进行深入研究.给出了航迹生存概率函数及局部重新规划区域的确定方法,且对变种群遗传算法、稀疏A*变步长搜索算法和动态规划方法进行了研究.并应用这三种改进后的算法对高超声速飞行器的航迹进行了规划,仿真结果表明,变种群遗传算法比较慢,只适合于离线规划,后两种算法应用在离线和在线实时规划时,都能快速的得到令人满意的航迹.     

多选择背包问题的求解算法探讨

本文简介了三种基本算法:动态规划、遗传算法、蚁群算法.给出了用这三种算法解决多选择背包问题的基本原理及求解步骤.并分别对其进行优缺点评述,指出在规模较大时用改进的遗传算法或蚁群算法较好.     

求解多段图问题的并行动态规划算法

多段图问题是一类特殊的单源最短路径问题。在串行动态规划算法的两种实现方法的基础上,根据图中顶点的编号,提出两种在集群环境下进行任务分割的并行化求解方法,并使用MPI进行实现。实验结果表明,所提出的算法具有较高的加速比和较低的通信复杂度、时间复杂度。算法不限于某种结构的集群,通用性强。     

考虑时间因素的0-1背包调度问题

文中提出考虑时间因素的0-1背包调度问题这一具有NP难度的组合优化问题。给定n个物体(每个物体i的重量为w_i,连续加工时间为t_i),以及一个容量为S的背包,要求给出一个调度方案(物品的放入顺序和放入时间),使得任意时刻放入背包的物品总重量不超过背包容量,每个物体需放入背包连续加工时长t_i后才能取出,该问题是求使所有物体均加工完毕的时间尽可能短的调度方案。提出了3种求解算法:迭代动态规划算法、基于分枝限界的完备算法和遗传进化算法。迭代动态规划算法使用动态规划策略放置尽可能多的未加工物体到背包中,然后每次迭代取出加工完成的物品后再使用动态规划放入尽可能多的剩余未加工物品,直至所有物品被加工完成。基于分枝限界的完备算法通过定义上下界及剪枝操作,有效地降低了算法的计算复杂度。遗传进化算法将一个物品装填序列定义为个体,并定义了相应的适应度、选择、交叉与变异操作。在所设计的3组共计36个算例上的实验结果表明,迭代动态规划算法可以很快求出高质量的解,基于分枝限界的完备算法对小规模算例有很好的效果,遗传算法在处理几百个物体的算例时能在1500s内得到比动态规划算法更好的结果。     

基于动态规划和遗传算法的混合算法研究

动态规划法和遗传算法是目前在水电站厂内经济运行中广泛应用的两种优化算法,文章提出了一种基于动态规划法和遗传算法的混合优化算法来分别解决大规模机组组合问题中空间最优化和时间最优化的计算机求解问题。避免了遗传算法计算速度缓慢的问题,又避免了动态规划法的“维数灾”问题。最后使用清江隔河岩水电站的4台机组的运行数据进行了仿真研究,并和完全使用动态规划法的结果进行了比较,获得了良好的效果,说明该混合优化算法对于厂内经济运行是一种可行的算法。     

0/1背包问题及其解法研究

0/1背包问题是实际当中经常遇到的一类经典NP-hard组合优化问题之一。本文分别从贪心方法、动态规划、回溯法、分枝-限界法,遗传算法这五种算法设计方法入手,概述了各种设计方法的基本原理,提出了求解0/1背包问题的算法思想,并对算法进行分析,提出了改进方法。     

0/1背包问题及其解法研究

0／1背包问题是实际当中经常遇到的一类经典NP—hard组合优化问题之一。本文分别从贪心方法、动态规划、回溯法、分枝-限界法．遗传算法这五种算法设计方法入手，概述了各种设计方法的基本原理，提出了求解0／1背包问题的算法思想，并对算法进行分析．提出了改进方法。     

Heuristics for minimizing maximum lateness on a single machine with family-dependent set-up times

We address the problem of scheduling a single machine subject to family-dependent set-up times in order to minimize maximum lateness. We present a number of local improvement heuristics based on the work of previous researchers, a rolling horizon heuristic, and an incomplete dynamic programming heuristic. Extensive computational experiments on randomly generated test problems compare the performance of these heuristics. The rolling horizon procedures perform particularly well but require their parameters to be set based on problem characteristics to obtain their best performance.     

Solving dynamic double row layout problem via combining simulated annealing and mathematical programming

Double row layout problem (DRLP) is to allocate facilities on two rows separated by a straight aisle. Aiming at the dynamic environment of product processing in practice, we propose a dynamic double-row layout problem (DDRLP) where material flows change over time in different processing periods. A mixed-integer programming model is established for this problem. A methodology combining an improved simulated annealing (ISA) with mathematical programming (MP) is proposed to resolve it. Firstly, a mixed coding scheme is designed to represent both of sequence of facilities and their exact locations. Secondly, an improved simulated annealing algorithm is suggested to produce a solution to DDRLP. Finally, MP is used to improve this solution by determining the optimal exact location for each facility. Experiments show that this methodology is able to obtain the optimal solutions for small size problems and outperforms an exact approach (CPLEX) for problems of realistic size.     

Solving facilities location problem in the presence of alternative processing routes using a genetic algorithm

Facilities location problem deals with the optimization of location of manufacturing facilities like machines, departments, etc. in the shop floor. This problem greatly affects performance of a manufacturing system. It is assumed in this paper that there are multiple products to be produced on several machines. Alternative processing routes are considered for each product and the problem is to determine the processing route of each product and the location of each machine to minimize the total distance traveled by the materials within the shop floor. This paper presents a mixed-integer non-linear mathematical programming formulation to find optimal solution of this problem. A technique is used to linearize the formulated non-linear model. However, due to the NP-hardness of this problem, even the linearized model cannot be optimally solved by the conventional mathematical programming methods in a reasonable time. Therefore, a genetic algorithm is proposed to solve the linearized model. The effectiveness of the GA approach is evaluated with numerical examples. The results show that the proposed GA is both effective and efficient in solving the attempted problem.     

Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm

This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the cell formation (CF) and group layout (GL) as the interrelated decisions involved in the design of a CMS in order to achieve an optimal (or near-optimal) design solution for a multi-floor factory in a multi-period planning horizon. Other design aspects are to design a multi-floor layout to form cells in different floors, a multi-rows layout of equal area facilities in each cell, flexible reconfigurations of cells during successive periods, distance-based material handling cost, and machine depot keeping idle machines. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, purchasing machines, machine processing, machine overhead, and machine relocation. Two numerical examples are solved by the CPLEX software to verify the performance of the presented model and illustrate the model features. Since this model belongs to NP-hard class, an efficient genetic algorithm (GA) with a matrix-based chromosome structure is proposed to derive near-optimal solutions. To verify its computational efficiency in comparison to the CPLEX software, several test problems with different sizes and settings are implemented. The efficiency of the proposed GA in terms of the objective function value and computational time is proved by the obtained results.     

An improved firefly algorithm for solving dynamic multidimensional knapsack problems

There is a wide range of publications reported in the literature, considering optimization problems where the entire problem related data remains stationary throughout optimization. However, most of the real-life problems have indeed a dynamic nature arising from the uncertainty of future events. Optimization in dynamic environments is a relatively new and hot research area and has attracted notable attention of the researchers in the past decade. Firefly Algorithm (FA), Genetic Algorithm (GA) and Differential Evolution (DE) have been widely used for static optimization problems, but the applications of those algorithms in dynamic environments are relatively lacking. In the present study, an effective FA introducing diversity with partial random restarts and with an adaptive move procedure is developed and proposed for solving dynamic multidimensional knapsack problems. To the best of our knowledge this paper constitutes the first study on the performance of FA on a dynamic combinatorial problem. In order to evaluate the performance of the proposed algorithm the same problem is also modeled and solved by GA, DE and original FA. Based on the computational results and convergence capabilities we concluded that improved FA is a very powerful algorithm for solving the multidimensional knapsack problems for both static and dynamic environments.     

A genetic algorithm-based approach for solving the resource-sharing and scheduling problem

We introduce a heuristic that is based on a unique genetic algorithm (GA) to solve the resource-sharing and scheduling problem (RSSP). This problem was previously formulated as a continuous-time mixed integer linear programming model and was solved optimally using a branch-and-bound (B&B) algorithm. The RSSP considers the use of a set of resources for the production of several products. Producing each product requires a set of operations with precedence relationships among them. Each operation can be performed using alternative modes which define the subset of the resources needed, and an operation may share different resources simultaneously. The problem is to select a single mode for each operation and accordingly to schedule the resources, while minimizing the makespan time. The GA we propose is based on a new encoding schema that adopts the structure of a DNA in nature. In our experiments we compared the effectiveness and runtime of our GA versus a B&B algorithm and two truncated B&B algorithms that we developed on a set of 118 problem instances. The results demonstrate that the GA solved all the problems (10 runs each), and reaches optimality in 75% of the runs, had an average deviation of less than 1% from the optimal makespan, and a runtime that was much less sensitive to the size of the problem instance.     

A dynamic programming algorithm for tree-like weighted set packing problem

In hierarchical organizations, hierarchical structures naturally correspond to nested sets. That is, we have a collection of sets such that for any two sets, either one of them is a subset of the other, or they are disjoint. In other words, a nested set system forms a hierarchy in the form of a tree structure. The task assignment problem on such hierarchical organizations is a real life problem. In this paper, we introduce the tree-like weighted set packing problem, which is a weighted set packing problem restricted to sets forming tree-like hierarchical structure. We propose a dynamic programming algorithm with cubic time complexity.     

应用遗传算法求解排课问题

给出了排课问题的描述和五元组定义.针对排课问题的关键因素,根据遗传算法的特点,设计了排课问题的编码方式,对求解的目标进行了优化,满足了实际应用.     

Finding optimal least-significant-bit substitution in image hiding by dynamic programming strategy

The processing of simple least-significant-bit (LSB) substitution embeds the secret image in the least significant bits of the pixels in the host image. This processing may degrade the host image quality so significantly that grabbers can detect that there is something going on in the image that interests them. To overcome this drawback, an exhaustive least-significant-bit substitution scheme was proposed by Wang et al. but it takes huge computation time. Wang et al. then proposed another method that uses a genetic algorithm to search “approximate” optimal solutions and computation time is no longer so huge. In this paper, we shall use the dynamic programming strategy to get the optimal solution. The experimental results will show that our method consumes less computation time and also gets the optimal solution.     

Learning context-free grammars using tabular representations

We present a novel algorithm using new hypothesis representations for learning context-free grammars from a finite set of positive and negative examples. We propose an efficient hypothesis representation method which consists of a table-like data structure similar to the parse table used in efficient parsing algorithms for context-free grammars such as Cocke-Younger-Kasami algorithm. By employing this representation method, the problem of learning context-free grammars from examples can be reduced to the problem of partitioning the set of nonterminals. We use genetic algorithms for solving this partitioning problem. Further, we incorporate partially structured examples to improve the efficiency of our learning algorithm, where a structured example is represented by a string with some parentheses inserted to indicate the shape of the derivation tree of the unknown grammar. We demonstrate some experimental results using these algorithms and theoretically analyse the completeness of the search space using the tabular method for context-free grammars.