A new heuristic algorithm for rectangle packing

The rectangle packing problem often appears in encasement and cutting as well as very large-scale integration design. To solve this problem, many algorithms such as genetic algorithm, simulated annealing and other heuristic algorithms have been proposed. In this paper, a new heuristic algorithm is recommended based on two important concepts, namely, the corner-occupying action and caving degree. Twenty-one rectangle-packing instances are tested by the algorithm developed, 16 of which having achieved optimum solutions within reasonable runtime. Experimental results demonstrate that the algorithm developed is fairly efficient for solving the rectangle packing problem.     

一种求解矩形块装填问题的拟人算法

在货物装载、木材下料、超大规模集成电路（VLSI）设计等工作中提出了矩形块装填与切割问题，对这一问题，国内外学者提出了诸如模拟退火算法、遗传算法及其它一些启发式算法等求解算法。本文利用人类的智慧和他们上万年以来形成的经验，提出了一种求解矩形块装填问题的拟人算法。谊算法使用了两个主要的思想策略，即矩形块选择策略和矩形块放置策略。用本文提出的算法，对21个测试算例进行了实算测试，测试结果表明：算法所得装填结果的优度高，计算时间短。对这21个测试算例。用本文算法计算，得到了其中16个算例的最优解，而计算时间都在2秒以内。进一步的测试表明，本文提出的算法对求解矩形块装填问题十分有效。     

一种求解多维0-1背包问题的拟人算法

在项目决策与规划,资源分配,货物装载等工作中,提出了多维0-1背包问题,对这一问题,国内外学者提出了诸如模拟退火算法,遗传算法,蚁群算法及其它一些启发式算法等求解算法。该文提出了一种新的启发式求解算法。该算法使用了两个主要的思想策略,即依据物品单位容积价值的高低选择物品并对其进行标记的策略和拟人跳坑策略。用本文提出的算法,对55个测试算例进行了实算测试,得到了其中54个算例的最优解。测试结果表明,用该文提出的拟人算法求解多维0-1背包问题,计算结果的优度高,计算时间短,是求解此问题的有效算法。     

An efficient heuristic algorithm for arbitrary shaped rectilinear block packing problem

Arbitrary shaped rectilinear block packing problem is a problem of packing a series of rectilinear blocks into a larger rectangular container, where arbitrary shaped rectilinear block is a polygonal block whose interior angle is either 90° or 270°. This problem involves many industrial applications, such as VLSI design, timber cutting, textile industry and layout of newspaper. Many algorithms based on different strategies have been presented to solve it. In this paper, we proposed an efficient heuristic algorithm which is based on principles of corner-occupying action and caving degree describing the quality of packing action. The proposed algorithm is tested on six instances from literatures and the results are rather satisfying. The computational results demonstrate that the proposed algorithm is rather efficient for solving the arbitrary shaped rectilinear block packing problem.     

求解矩形packing问题的贪心算法

在货物装载、木材下料、超大规模集成电路设计等工作中提出了矩形packing问题。对这一问题,国内外学者提出了诸如模拟退火算法、遗传算法及其它一些启发式算法等求解算法。该文利用人类的智慧及历史上形成的经验,提出了一种求解矩形packing问题的贪心算法。并对21个公开测试实例进行了实算测试,所得结果的平均面积未利用率为0.28%,平均计算时间为17.86s,并且还得到了其中8个实例的最优解。测试结果表明,该算法对求解矩形packing问题相当有效。     

Minimizing job completion time variance for service stability on identical parallel machines

This paper addresses a job scheduling problem on multiple identical parallel machines so as to minimize job completion time variance (CTV). CTV minimization is closely related to the Just-In-Time philosophy and the service stability concept since it penalizes both earliness and tardiness. Its applications can be found in many real-life areas such as Internet data packet dispatching and production planning. This paper focuses on the unrestricted case of the problem where idle times are allowed to exist before machines start to process jobs. We prove several dominant properties about the optimal solution to the problem. For instance, we prove that the mean completion time (MCT) on each machine should be the same under an optimal schedule. Based on these properties, an efficient heuristic algorithm is proposed. Computational experiments are conducted to test the performance of the proposed algorithm. The outputs demonstrate that the proposed algorithm is near optimal for small problem instances and greatly outperforms some existing algorithms for large problem instances.     

用于求解二维圆形排样问题的自适应混合算法

针对二维圆形版面不等圆排样问题,在最小局部距离定位布局策略的基础上,引入紧凑度和适应度,提出基于拟矩形排样的自适应启发式算法,并与以自然数编码的遗传算法相结合构建混合算法。该混合算法发挥两者的全局搜索能力与局部寻优能力。在标准测试算例上,与一些经典算法进行比较,结果表明,该算法能够在更短的时间内获得更为满意的结果。     

A heuristic algorithm based on Monte Carlo methods for the Rural Postman Problem

In Routing Problems the aim is to determine a minimum cost traversal over a graph satisfying some specified constraints. Most of them are NP-hard problems and many different heuristic solution algorithms have been proposed. The name Monte Carlo, MC, applies to a set of heuristic procedures with the common feature of using random numbers to simulate a given process. MC approach has not been applied to the framework of Routing Problems in the literature. The purpose of this paper is to demonstrate that MC methods could be useful in implementing heuristic algorithms for Routing Problems. In particular, we design an efficient MC heuristic algorithm for the well known Rural Postman Problem (RPP), for which we have a set of instances with known optimal solution taken from the literature.The Rural Postman Problem (RPP) consists of finding a minimum cost traversal of a specified arc subset of a graph. Given that the RPP is a NP-hard problem, heuristic algorithms are interesting both to handle large size instances and to provide upper bounds that could be used in branch and cut procedures. In this paper we propose a heuristic algorithm for the RPP based on Monte Carlo methods. We simulate a vehicle travelling randomly over the graph, jumping from one node to another on the basis of certain probabilities. Monte Carlo methods provide a simple approach to many different Routing Problems and they are easily implemented in a computer code. The application of this algorithm to a set of RPP instances taken from the literature demonstrates that, using the appropriate probabilities, they are also efficient.     

作业车间调度问题的一种混合式算法

描述了一种解决作业车间调度最短完工时间问题的混合式算法．该算法基于禁忌搜索和转换瓶颈技术．算法中利用了多种禁忌搜索方法．为了得到更好的结果，算法中还引入了倒转技术．从对一组问题基准实例的实验计算结果看，该算法在合理的计算时间内，对多个实例得到比当前解决该问题的最高效的启发式算法之一的TSSB算法更好的结果．     

An effective two-stage simulated annealing algorithm for the minimum linear arrangement problem

In this paper, an improved two-stage simulated annealing algorithm is presented for the minimum linear arrangement problem for graphs. This algorithm integrates several distinguished features including an efficient heuristic to generate good quality initial solutions, a highly discriminating evaluation function, a special neighborhood function and an effective cooling schedule. The algorithm is evaluated on a set of 30 well-known benchmark instances of the literature and compared with several state-of-the-art algorithms, showing improvements of 17 previous best results.     

基于核心化技术的点覆盖改进算法

点覆盖是一个著名的NP难解问题，在通信网络和生物信息学等领域具有重要应用。针对点覆盖的研究主要集中在启发式或近似算法，其主要不足是无法实现全局最优。核心化是处理难解问题的一种新方法。提出融合启发式操作和核心化操作的算法框架，利用核心化技术进行点覆盖启发式算法优化。核心化操作挖掘出全局最优的顶点集，而启发式操作改变网络拓扑，使下一轮核心化操作能够继续，两者交叉执行实现解精度优化。实验结果表明，提出的算法在不同网络中均能实现不同程度的优化，在几乎所有稀疏网络实例中获得了最优解。     

基于禁忌搜索的启发式算法求解球体Packing问题*

为求解具有NP难度的球体Packing问题,通过将禁忌搜索方法与基于自适应步长的梯度下降法和二分法相结合,提出了一个启发式算法。对50个等球算例进行了实例测试,算法改进了其中44个算例的目前最优结果。大量的实例计算结果表明,该启发式算法是求解球体Packing问题的一个有效算法。     

An effective heuristic algorithm for the maximum satisfiability problem

Stochastic local search algorithms (SLS) have been increasingly applied to approximate solutions of the weighted maximum satisfiability problem (MAXSAT), a model for solutions of major problems in AI and combinatorial optimization. While MAXSAT instances have generally a strong intrinsic dependency between their variables, most of SLS algorithms start the search process with a random initial solution where the value of each variable is generated independently with the same uniform distribution. In this paper, we propose a new SLS algorithm for MAXSAT based on an unconventional distribution known as the Bose-Einstein distribution in quantum physics. It provides a stochastic initialization scheme to an efficient and very simple heuristic inspired by the co-evolution process of natural species and called Extremal Optimization (EO). This heuristic was introduced for finding high quality solutions to hard optimization problems such as colouring and partitioning. We examine the effectiveness of the resulting algorithm by computational experiments on a large set of test instances and compare it with some of the most powerful existing algorithms. Our results are remarkable and show that this approach is appropriate for this class of problems.     

基于CW节约算法的ERP系统改进研究

ERP(enterprise resource planning)系统在现代企业管理中的重要性已成为企业界的共识,但是随着物流业的发展,目前ERP缺少对VRP(vehicle routing problem)支持的问题则成为影响其发展的一个重要因素.通过在当前ERP系统加入车辆调度模块,对此提出了相应的改进方案.当然由于车辆调度的不确定性,需采用启发式算法,最后确定采用易于扩展和实现的CW(clarke-wright)节约算法.针对ERP系统目前向B/S架构发展的趋势,使用具有跨平台运行优势的J2EE平台来实现该系统.     

A comparative runtime analysis of heuristic algorithms for satisfiability problems

The satisfiability problem is a basic core NP-complete problem. In recent years, a lot of heuristic algorithms have been developed to solve this problem, and many experiments have evaluated and compared the performance of different heuristic algorithms. However, rigorous theoretical analysis and comparison are rare. This paper analyzes and compares the expected runtime of three basic heuristic algorithms: RandomWalk, (1+1) EA, and hybrid algorithm. The runtime analysis of these heuristic algorithms on two 2-SAT instances shows that the expected runtime of these heuristic algorithms can be exponential time or polynomial time. Furthermore, these heuristic algorithms have their own advantages and disadvantages in solving different SAT instances. It also demonstrates that the expected runtime upper bound of RandomWalk on arbitrary k-SAT (k?3) is O(n(k−1)), and presents a k-SAT instance that has Θ(n(k−1)) expected runtime bound.     

DVBMT问题的一种改进算法

研究多播端到端时延受限条件下的最优时延抖动问题,目前已经出现了许多启发式算法,如DVMA(delay variation multicast algorithm)、DDVCA(delay and delay variation constraint algorithm)。DDVCA的时延抖动小于DVMA。Cheng等人也提出了一种算法,它的时延抖动小于DDVCA。在此基础上提出了一种有效的多播路由算法。仿真结果表明,该算法的平均时延抖动小于Cheng等人的平均时延抖动。     

求解三维装箱问题的混合模拟退火算法

提出了一个高效求解三维装箱问题(Three Dimensional Container Loading Problem 3D-CLP)的混合模拟退火算法.三维装箱问题要求装载给定箱子集合的一个子集到容器中,使得被装载的箱子总体积最大.文中介绍的混合模拟退火算法基于三个重要算法:(1)复合块生成算法,与传统算法不同的是文中提出的复合块不只包含单一种类的箱子,而是可以在一定的限制条件下包含任意种类的箱子.(2)基础启发式算法,该算法基于块装载,可以按照指定装载序列生成放置方案.(3)模拟退火算法,以复合块生成和基础启发式算法为基础,将装载序列作为可行放置方案的编码,在编码空间中采用模拟退火算法进行搜索以寻找问题的近似最优解.文中采用1500个弱异构和强异构的装箱问题数据对算法进行测试.实验结果表明,混合模拟退火算法的填充率超过了目前已知的优秀算法.     

An improved fireworks algorithm for the capacitated vehicle routing problem

The capacitated vehicle routing problem (CVRP), which aims at minimizing travel costs, is a wellknown NP-hard combinatorial optimization. Owing to its hardness, many heuristic search algorithms have been proposed to tackle this problem. This paper explores a recently proposed heuristic algorithm named the fireworks algorithm (FWA), which is a swarm intelligence algorithm. We adopt FWA for the combinatorial CVRP problem with several modifications of the original FWA: it employs a new method to generate "sparks" according to the selection rule, and it uses a new method to determine the explosion amplitude for each firework. The proposed algorithm is compared with several heuristic search methods on some classical benchmark CVRP instances. The experimental results show a promising performance of the proposed method. We also discuss the strengths and weaknesses of our algorithm in contrast to traditional algorithms.     

Bacterial Foraging Optimization Approach to Portfolio Optimization

In this paper we propose a heuristic approach based on bacterial foraging optimization (BFO) in order to find the efficient frontier associated with the portfolio optimization (PO) problem. The PO model with cardinality and bounding constraints is a mixed quadratic and integer programming problem for which no exact algorithms can solve in an efficient way. Consequently, various heuristic algorithms, such as genetic algorithms and particle swarm optimization, have been proposed in the past. This paper aims to examine the potential of a BFO algorithm in solving the PO problem. BFO is a new swarm intelligence technique that has been successfully applied to several real world problems. Through three operations, chemotaxis, reproduction, and elimination-dispersal, the proposed BFO algorithm can effectively solve a PO problem. The performance of the proposed approach was evaluated in computational tests on five benchmark data sets, and the results were compared to those obtained from existing heuristic algorithms. The proposed BFO algorithm is found to be superior to previous heuristic algorithms in terms of solution quality and time.     

一个解决集合覆盖问题的二阶段遗传算法

针对集合覆盖问题,提出一个高效的可解决大规模数据的二阶段遗传算法.二阶段遗传算法可以分为数据约简阶段和启发式求解阶段,论文形式化地描述了数据约简阶段的相关定义、定理和算法,证明了该约简方法的有效性;并给出了启发式求解阶段中针对集合覆盖问题的遗传算法中选择、交叉、变异算子的设计方法.对Beasley提出的45个测试用例的测试结果验证了二阶段遗传算法的求解效率和求解质量高于其它遗传算法.