允许自由旋转的2个简单多边形匹配算法

为了解决二维不规则排料问题中的匹配问题,提出了一种允许自由旋转条件下,2个无孔洞的简单多边形之间的匹配算法.该算法基于2个多边形可以自由旋转的假设,对它们之间NFP为凹或凸的情况,选择适当的匹配方法,找出一种使得其匹配后空隙尽可能小,同时保证其整体的矩形的规整度也较高的匹配方案;并用匹配空隙的利用率、匹配后整体面积的利用率,以及匹配后整体的矩形规整度等多个指标来衡量匹配的效果.实验选择于ESICUP中的部分代表性的多边形样例与多个算法进行对比实验,结果表明,该算法在任意旋转精度的要求下,均具有运行速度快的特点,可以很好地应用于服装排料等实际问题.     

An improved method for calculating the no-fit polygon

The no-fit polygon (NFP) is the set of feasible locations that one polygon may take with respect to another polygon, such that the polygons do not overlap. Feasible locations are required for most of the solutions to two-dimensional packing problems, and also for other problems such as robot motion planning.     

Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

Global derivative-free deterministic algorithms are particularly suitable for simulation-based optimization, where often the existence of multiple local optima cannot be excluded a priori, the derivatives of the objective functions are not available, and the evaluation of the objectives is computationally expensive, thus a statistical analysis of the optimization outcomes is not practicable. Among these algorithms, particle swarm optimization (PSO) is advantageous for the ease of implementation and the capability of providing good approximate solutions to the optimization problem at a reasonable computational cost. PSO has been introduced for single-objective problems and several extension to multi-objective optimization are available in the literature. The objective of the present work is the systematic assessment and selection of the most promising formulation and setup parameters of multi-objective deterministic particle swarm optimization (MODPSO) for simulation-based problems. A comparative study of six formulations (varying the definition of cognitive and social attractors) and three setting parameters (number of particles, initialization method, and coefficient set) is performed using 66 analytical test problems. The number of objective functions range from two to three and the number of variables from two to eight, as often encountered in simulation-based engineering problems. The desired Pareto fronts are convex, concave, continuous, and discontinuous. A full-factorial combination of formulations and parameters is investigated, leading to more than 60,000 optimization runs, and assessed by three performance metrics. The most promising MODPSO formulation/parameter is identified and applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions. Its performance is finally compared with four stochastic algorithms, namely three versions of multi-objective PSO and the genetic algorithm NSGA-II.     

Budget Allocation for Maximizing Viral Advertising in Social Networks

Viral advertising in social networks has arisen as one of the most promising ways to increase brand awareness and product sales. By distributing a limited budget, we can incentivize a set of users as initial adopters so that the advertising can start from the initial adopters and spread via social links to become viral. Despite extensive researches in how to target the most influential users, a key issue is often neglected: how to incentivize the initial adopters. In the problem of influence maximization, the assumption is that each user has a fixed cost for being initial adopters, while in practice, user decisions for accepting the budget to be initial adopters are often probabilistic rather than deterministic. In this paper, we study optimal budget allocation in social networks to maximize the spread of viral advertising. In particular, a concave probability model is introduced to characterize each user’s utility for being an initial adopter. Under this model, we show that it is NP-hard to find an optimal budget allocation for maximizing the spread of viral advertising. We then present a novel discrete greedy algorithm with near optimal performance, and further propose scaling-up techniques to improve the time-efficiency of our algorithm. Extensive experiments on real-world social graphs are implemented to validate the effectiveness of our algorithm in practice. The results show that our algorithm can outperform other intuitive heuristics significantly in almost all cases.     

A biased random key genetic algorithm for open dimension nesting problems using no-fit raster

We consider two NP-hard open dimension nesting problems for which a set of items has to be packed without overlapping into a two-dimensional bin in order to minimize one or both dimensions of this bin. These problems are faced by real-life applications, such as textile, footwear and automotive industries. Therefore, there is a need for specialized systems to help in a decision making process. Bearing this in mind, we derive new concepts as the no-fit raster, which can be used to check overlapping between any two-dimensional generic-shaped items. We also use a biased random key genetic algorithm to determine the sequence in which items are packed. Once the sequence of items is determined, we propose two heuristics based on bottom-left moves and the no-fit raster concept, which are in turn used to arrange these items into the given bin observing the objective criteria. As far as we know, the problem with two-open dimensions is being solved for the first time in the context of nesting problems and we present the first whole quadratic model for this problem. Computational experiments conducted on benchmark instances from the literature (some from the textile industry and others including circles, convex, and non-convex polygons) show the competitiveness of the approaches developed as they were able to calculate the best results for 74.14% of the instances. It can be observed that these results show new directions in terms of solving nesting problems whereby approaches can be coupled in existing intelligent systems to support decision makers in this field.     

Timetabling through Constrained Heuristic Search and Genetic Algorithms

Hybrid genetic algorithms are presented that use constrained heuristic search and genetic techniques for the timetabling problem (TP). The TP is an NP-hard problem for which a general polynomial time deterministic algorithm is not known. The paper describes the classification of constraints and the constraint ordering to obtain the minimization of backtracking and the maximization of parallelism. The school timetabling problem is discussed in detail as a case study. The genetic algorithm approach is particularly well suited to this kind of problem, since there exists an easy way to assess a good timetable, but not a well structured automatic technique for constructing it. So, a population of timetables is created that evolves toward the best solution. The evaluation function and the genetic operators are well separated from the domain-specific parts, such as the knowledge of the problem and the heuristics, i.e. from the timetable builder. The present paper illustrates an approach based on the hybridization of constrained heuristic search with novel genetic algorithm techniques. It compares favourably with known programs to solve decision problems under logic constraints. The cost of the new algorithm and the quality of the solutions obtained in significant experiments are reported.     

A nearly optimal algorithm for covering the interior of an Art Gallery

The problem of locating visual sensors can be often modelled as 2D Art Gallery problems. In particular, tasks such as surveillance require observing the interior of a polygonal environment (interior covering, IC), while for inspection or image based rendering observing the boundary (edge covering, EC) is sufficient. Both problems are NP-hard, and no technique is known for transforming one problem into the other. Recently, an incremental algorithm for EC has been proposed, and its near-optimality has been demonstrated experimentally. In this paper we show that, with some modification, the algorithm is nearly optimal also for IC. The algorithm has been implemented and tested over several hundreds of random polygons with and without holes. The cardinality of the solutions provided is very near to, or coincident with, a polygon specific lower bound, and then suboptimal or optimal. In addition, our algorithm has been compared, for all the test polygons, with recent heuristic sensor location algorithms. In all cases, the cardinality of the set of guards provided by our algorithm was less than or equal to that of the set computed by the other algorithms. An enhanced version of the algorithm, also taking into account range and incidence constraints, has also been implemented.     

A linear programming formulation with integer solutions for solving an equipment replacement problem with multiple assets: the concave demand case

A linear programming formulation is presented for the deterministic equipment replacement problem in which multiple assets are required each period and a number of assets are available for replacement. Under common cost assumptions, the linear programming solutions are shown to be integer for certain demand constraints- This paper considers the case of concave demand over a finite horizon. The integer solutions allow for implementable decisions and the formulation allows for the solution of large replacement problems, such as vehicles in a fleet, without the computational effort of branch-and-bound procedures. Numerical solutions are provided for illustration of the formulation and its efficiency in solving large replacement problems.     

Visiting convex regions in a polygonal map

This paper is concerned with a variant of the multi-goal path planning in which goals are represented as convex polygons. The problem is to find a closed shortest path in a polygonal map such that all goals are visited. The proposed solution is based on a self-organizing map (SOM) algorithm for the traveling salesman problem. Neurons’ weights are considered as nodes inside the polygonal domain and connected nodes represent a path that evolves according to the proposed adaptation rules. In addition, a reference algorithm based on the solution of the traveling salesman problem and the consecutive touring polygons problem is provided to find high quality solutions of the created set of problems. The problems are designed to represent various inspection and patrolling tasks and can form a kind of benchmark set for multi-goal path planning algorithms. The performance of the algorithms is examined in this problem set, which includes an instance of the watchman route problem with restricted visibility range. The proposed SOM based algorithms provide a unified approach to solve various visibility based routing problems in polygonal maps while they provide a competitive quality of solutions to the reference algorithm with significantly lower computational requirements.     

Improved Tabu search heuristics for the dynamic space allocation problem

The dynamic space allocation problem (DSAP) presented in this paper considers the task of assigning items (resources) to locations during a multi-period planning horizon such that the cost of rearranging the items is minimized. Three tabu search heuristics are presented for this problem. The first heuristic is a simple basic tabu search heuristic. The second heuristic adds diversification and intensification strategies to the first, and the third heuristic is a probabilistic tabu search heuristic. To test the performances of the heuristics, a set of test problems from the literature is used in the analysis. The results show that the tabu search heuristics are efficient techniques for solving the DSAP. More importantly, the proposed tabu search heuristic with diversification/intensification strategies found new best solutions using less computation time for one-half of all the test problems.     

Hybrid genetic algorithms for timetabling

Hybrid genetic algorithms are presented that use optimization heuristics and genetic techniques to outperform all existing programs for the timetabling problem. The timetabling problem is very hard (NP-complete) and a general polynomial time deterministic algorithm is not known. An artificial intelligence approach, in a logic programming environment, may be useful for such a problem. The decomposition and classification of constraints and the constraint ordering to obtain the minimization of the backtracking and the maximization of the parallelism are illustrated. The school timetabling problem is discussed in detail as a case study. The genetic algorithm approach is particularly well suited for this kind of problem, since there exists an easy way to assess a good timetable but not a well-structured automatic technique for constructing it. So, a population of timetables is created that evolves toward the best solutions. The evaluation function and the genetic operators are well separated from the domain-specific parts, such as the problem knowledge and the heuristics, i.e., from the timetable builder. A fundamental issue and a general problem in the decision process and automated reasoning is how to efficiently obtain logic decisions under disjunctive constraints. Logic constraint satisfaction problems are in general NP-hard and a general deterministic polynomial time algorithm is not known. The present article illustrates an approach based on the hybridization of constrained heuristic search with novel genetic algorithm techniques. It compares favorably with the best-known programs to solve decisions problems under logic constraints. Complexity of the new algorithms and results of significant experiments are reported. © 1996 John Wiley & Sons, Inc.     

一个有效的多边形裁剪算法

多边形裁剪与线剪裁相比具有更广泛的实用意义,因此它是目前裁剪研究的主要课题.提出了一个多边形裁剪多边形的有效算法.其中的多边形都可以是一般多边形,既可以是凹多边形,也可以是有内孔的多边形.该算法不仅可以求多边形的"交"(多边形裁剪),而且可以求多边形的"并"和"差".它是以所提出的一系列新方法和新技术为基础而形成的.首先,该算法使用单线性链表数据结构,与其他使用双链表或树结构的算法相比,具有占用空间少及处理速度快的特点;其次,找到了两个多边形之间进、出点之间的关系.再通过合理的数据结构处理,减少了算法对多边形链表的遍历次数,而且允许多边形既可以按顺时针方向也可以按逆时针方向输入.最后,判断和计算交点是裁剪算法的主要工作.提出了一个具有最少计算量的交点判断和计算方法,进一步加快了算法的运行速度.与其他同类算法进行了比较,结果表明,新算法具有最简单的结构和最快的执行速度.     

计算多边形交集、并集面积的算法

平面多边形交集与并集面积的计算机算法可以利用多边形裁剪算法来实现。本文提出的算法思想是利用Weiler-Atherton多边形裁剪算法中的多边形链表,在遍历链表时遇到交点就改变跟踪方向,这样可以求出并集顶点表,求交集时只要从入点开始跟踪遇到交点再改变跟踪方向;最后,通过交集和并集表求出它们的面积。多边形可以是凸的或凹的、甚至是带孔的。     

一种加权剖分简单多边形为三角形和凸四边形子域的算法

针对计算几何与有限元网格自动剖分中多边形子域剖分问题，给出了一种适用于有限元网格子域单元（即大单元）剖分的标准，并提出了一种通过在可视点对之间引进适当的多边形剖分和根据子域单元的形状质量判定因子来引导剖分的算法。由于建立的权函数和凹角（凸角）本身有关，因此对同属于凹角（凸角）的权函数也可以加以权值上的区分。该算法通过分步进行剖分，即先将简单多边形剖分为凸多边形，然后再将凸多边形剖分为凸六边形和凸五边形，最后将凸六边形和凸五边形剖分为三角形和凸四边形，以得到满足要求的剖分结果。在以上的每个剖分过程中，都引进了权重来引导剖分，使得剖分结果更加优化、合理。     

格网划分的双策略跟踪多边形裁剪算法

论文提出了一种高效稳定的多边形裁剪算法,算法支持带内环的平面简单 多边形,同时也支持多边形的“并”和“差”等布尔运算。首先,设计了算法所需的数据结构; 其次,基于直线扫描转换Bresenham 算法原理提出了边网格划分的有效算法,并应用一个简 单的方法避免不同网格内边的重复求交;最后,将交点分类为普通交点和顶交点,并针对这 两类交点构造了不同的跟踪策略,在跟踪过程中交替、递归地应用这两个策略来确保算法处 理特殊情况时的稳定性。与其它同类算法的比较表明,新算法具有更高的效率。     

GRASP with path relinking for the symmetric Euclidean clustered traveling salesman problem

In this paper, we propose new heuristics using several path-relinking strategies to solve the Clustered Traveling Salesman Problem (CTSP). The CTSP is a generalization of the Traveling Salesman Problem (TSP) in which the set of vertices is partitioned into clusters and the objective is to find a minimum cost Hamiltonian cycle such that the vertices of each cluster are visited continuously. A comparison among the performance of the several different adopted path-relinking strategies is presented using instances with up to 2000 vertices and clusters varying between 4 and 150 vertices. Also computational experiments were performed to compare the performance of the proposed heuristics with an exact algorithm and a Genetic Algorithm. The obtained computational results showed that the proposed heuristics were able to obtain competitive results related to the quality of the solutions and computational execution time.     

Translational polygon containment and minimal enclosure using mathematical programming

A new algorithm is given for the two-dimensional translational containment problem: find translations for k polygons which place them inside a polygonal container without overlapping. Both the polygons and the container can be non-convex. The algorithm is based on mathematical programming principles. The containment algorithm is generalized to solve minimal enclosure problems: find the minimal area enclosing square or minimal area enclosing rectangle for k translating polygons. The containment algorithm consists of new algorithms for restriction , evaluation , and subdivision of two-dimensional configuration spaces. Restriction establishes lower bounds through relaxation and the solution of linear programs. Evaluation establishes upper bounds by finding potential solutions. Subdivision branches, when necessary, by introducing a cutting plane. The algorithm shows that either the upper bound of the objective (overlap) is exactly zero or the lower bound is greater than zero. Hence, it gives an exact solution to the containment problem. Experiments show that new containment algorithm clearly outperforms purely geometric containment algorithms. For data sets from the apparel industry, it can solve containment for up to ten non-convex polygons in practice. An implementation of the algorithm given in this paper has been licensed by Gerber Garment Technologies, the largest provider of textile CAD/CAM software in the US, and they are incorporating it into an existing CAD/CAM software product.     

Simultaneous Optimization via Approximate Majorization for Concave Profits or Convex Costs

For multicriteria problems and problems with a poorly characterized objective, it is often desirable to approximate simultaneously the optimum solution for a large class of objective functions. We consider two such classes: (1) Maximizing all symmetric concave functions. (2) Minimizing all symmetric convex functions. The first class corresponds to maximizing profit for a resource allocation problem (such as allocation of bandwidths in a computer network). The concavity requirement corresponds to the law of diminishing returns in economics. The second class corresponds to minimizing cost or congestion in a load balancing problem, where the congestion/cost is some convex function of the loads. Informally, a simultaneous α-approximation for either class is a feasible solution that is within a factor α of the optimum for all functions in that class. Clearly, the structure of the feasible set has a significant impact on the best possible α and the computational complexity of finding a solution that achieves (or nearly achieves) this α. We develop a framework and a set of techniques to perform simultaneous optimization for a wide variety of problems. We first relate simultaneous α-approximation for both classes to α-approximate majorization. Then we prove that α-approximately majorized solutions exist for logarithmic values of α for the concave profits case. For both classes, we present a polynomial-time algorithm to find the best α if the set of constraints is a polynomial-sized linear program and discuss several non-trivial applications. These applications include finding a (log n)-majorized solution for multicommodity flow, and finding approximately best α for various forms of load balancing problems. Our techniques can also be applied to produce approximately fair versions of the facility location and bi-criteria network design problems. In addition, we demonstrate interesting connections between distributional load balancing (where the sizes of jobs are drawn from known probability distributions but the actual size is not known at the time of placement) and approximate majorization.     

On multiprocessor task scheduling using efficient state space search approaches

Obtaining an optimal schedule for a set of precedence-constrained tasks is a well-known NP-complete problem in its general form. In view of the intractability of the problem, most of the previous work relies on heuristics that try to find reasonably high quality solutions in an acceptable amount of time. While optimal polynomial-time algorithms are known only for a few simple cases (and in other cases can only be obtained through an exhaustive search with prohibitively high time complexity), they may be critically important for applications in which performance is the prime objective. Optimal solutions can also serve as a reference to test the performance of various heuristics. Moreover, an optimal schedule for a program at hand needs to be determined only once (and off-line) but the program using that schedule is in general executed several times. In this paper, we propose optimal algorithms for static scheduling of task graphs with arbitrary parameters to multiple homogeneous processors. The first algorithm is based on the A^* search technique and uses a computationally efficient cost function for guiding the search with reduced complexity. Additionally, we propose a number of effective state-pruning techniques to reduce the search space. For further lowering the complexity, we propose an efficient parallelization of the search algorithm. We parallelize the algorithm with reduced interprocessor communication as well as with static and dynamic load-balancing schemes to evenly distribute the search states to the processors. We also propose an approximate algorithm that guarantees a bounded deviation from the optimal solution but executes in a considerably shorter time. Based on an extensive experimental evaluation of the algorithms, we conclude that the parallel algorithm with pruning techniques is an efficient scheme for generating optimal solutions of reasonably large problems while the approximate algorithm is effective if slightly degraded solutions are acceptable.     

Translation separability of sets of polygons

We consider the problem of separating a set of polygons by a sequence of translations (one such collision-free translation motion for each polygon). If all translations are performed in a common direction the separability problem so obtained has been referred to as the uni-directional separability problem; for different translation directions, the more general multi-directional separability problem arises. The class of such separability problems has been studied previously and arises e.g. in computer graphics and robotics. Existing solutions to the uni-directional problem typically assume the objects to have a certain predetermined shape (e.g., rectangular or convex objects), or to have a direction of separation already available. Here we show how to compute all directions of unidirectional separability for sets of arbitrary simple polygons.The problem of determining whether a set of polygons is multi-directionally separable had been posed by G.T. Toussaint. Here we present an algorithm for solving this problem which, in addition to detecting whether or not the given set is multidirectionally separable, also provides an ordering in which to separate the polygons. In case that the entire set is not multi-directionally separable, the algorithm will find the largest separable subset.Research supported by NSERC under grant No. A9173 and A0392, respectively