Combining Swaps and Node Weights in an Adaptive Greedy Approach for the Maximum Clique Problem

In this work, the NP-hard maximum clique problem on graphs is considered. Starting from basic greedy heuristics, modifications and improvements are proposed and combined in a two-phase heuristic procedure. In the first phase an improved greedy procedure is applied starting from each node of the graph; on the basis of the results of this phase a reduced subset of nodes is selected and an adaptive greedy algorithm is repeatedly started to build cliques around such nodes. In each restart the selection of nodes is biased by the maximal clique generated in the previous execution. Computational results are reported on the DIMACS benchmarks suite. Remarkably, the two-phase procedure successfully solves the difficult Brockington-Culberson instances, and is generally competitive with state-of-the-art much more complex heuristics.     

Optimized Crossover-Based Genetic Algorithms for the Maximum Cardinality and Maximum Weight Clique Problems

In Balas and Niehaus (1996), we have developed a heuristic for generating large cliques in an arbitrary graph, by repeatedly taking two cliques and finding a maximum clique in the subgraph induced by the union of their vertex sets, an operation executable in polynomial time through bipartite matching in the complement of the subgraph. Aggarwal, Orlin and Tai (1997) recognized that the latter operation can be embedded into the framework of a genetic algorithm as an optimized crossover operation. Inspired by their approach, we examine variations of each element of the genetic algorithm—selection, population replacement and mutation—and develop a steady-state genetic algorithm that performs better than its competitors on most problems.     

求解最大团问题的熵正则化方法

最大团问题是组合优化的一个经典问题.在Motzkin和Straus的二次规划模型基础上,给出一种求解该问题的熵正则化算法.引进熵函数有两个目的,一是将问题的求解纳入信息论方法的框架,二是通过它的引进改善问题的凸性.几个标准考题的计算结果表明,该算法稳定有效.     

求解最大团问题的D函数正则化方法

最大团问题是一个经典的组合优化问题.在Motzkin和Straus的二次规划模型基础上,给出一种求解该问题的D函数正则化算法.通过引进D函数可以改善问题的凸性.几个标准考题的计算结果表明,该算法稳定有效.     

Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming

In this paper, a greedy heuristic and two local search algorithms, 1-opt local search and k-opt local search, are proposed for the unconstrained binary quadratic programming problem (BQP). These heuristics are well suited for the incorporation into meta-heuristics such as evolutionary algorithms. Their performance is compared for 115 problem instances. All methods are capable of producing high quality solutions in short time. In particular, the greedy heuristic is able to find near optimum solutions a few percent below the best-known solutions, and the local search procedures are sufficient to find the best-known solutions of all problem instances with n 100. The k-opt local searches even find the best-known solutions for all problems of size n 250 and for 11 out of 15 instances of size n = 500 in all runs. For larger problems (n = 500, 1000, 2500), the heuristics appear to be capable of finding near optimum solutions quickly. Therefore, the proposed heuristics—especially the k-opt local search—offer a great potential for the incorporation in more sophisticated meta-heuristics.     

An Efficient Branch-and-bound Algorithm for Finding a Maximum Clique with Computational Experiments

We present an exact and efficient branch-and-bound algorithm MCR for finding a maximum clique in an arbitrary graph. The algorithm is not specialized for any particular type of graph. It employs approximate coloring to obtain an upper bound on the size of a maximum clique along with an improved appropriate sorting of vertices. We demonstrate by computational experiments on random graphs with up to 15,000 vertices and on DIMACS benchmark graphs that in general, our algorithm decidedly outperforms other existing algorithms. The algorithm has been successfully applied to interesting problems in bioinformatics, image processing, design of quantum circuits, and design of DNA and RNA sequences for biomolecular computation.     

Greedy Randomized Adaptive Search Procedures

Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search Procedures). GRASP is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand. The incumbent solution over all GRASP iterations is kept as the final result. There are two phases within each GRASP iteration: the first intelligently constructs an initial solution via an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in hope of finding an improvement. In this paper, we define the various components comprising a GRASP and demonstrate, step by step, how to develop such heuristics for combinatorial optimization problems. Intuitive justifications for the observed empirical behavior of the methodology are discussed. The paper concludes with a brief literature review of GRASP implementations and mentions two industrial applications.     

Heuristics for the Maximum Outerplanar Subgraph Problem

Determining the maximum outerplanar subgraph of a given graph is known to be an NP-complete problem. In the literature there are no earlier experiment on approximating the maximum outerplanar subgraph problem. In this paper we compare solution quality and running times of different heuristics for finding maximum outerplanar subgraphs. We compare a greedy heuristic against a triangular cactus heuristic and its greedy variation. We also use the solutions from the greedy heuristics as initial solutions for a simulated annealing algorithm.The main experimental result is that simulated annealing with initial solution taken from the greedy triangular cactus heuristic yields the best known approximations for the maximum outerplanar subgraph problem.Work funded by the Tampere Graduate School in Information Science and Engineering (TISE) and supported by the Academy of Finland (Project 51528).     

Clique inference process for solving Max-CSP

Few works on problems CSP, Max-CSP and weighted CSP was carried out in the field of Combinatorial Optimization, whereas this field contains many algorithmic tools which can be used for the resolution of these problems.In this paper, we introduce the binary clique concept: clique which expresses incompatibilities between values of two CSP variables. We propose a linear formulation for any binary clique and we present several ways to exploit it in order to compute lower bounds or to solve Max-CSP. We also show that the binary clique concept can be exploited in the weighted CSP framework.The obtained theoretical and experimental results are very interesting and they open new prospects to exploit the Combinatorial Optimization algorithmic tools for the resolution of CSP, Max-CSP and weighted CSP.     

Heuristics for Distribution Network Design in Telecommunication

A distribution network problem arises in a lower level of an hierarchical modeling approach for telecommunication network planning. This paper describes a model and proposes a lagrangian heuristic for designing a distribution network. Our model is a complex extension of a capacitated single commodity network design problem. We are given a network containing a set of sources with maximum available supply, a set of sinks with required demands, and a set of transshipment points. We need to install adequate capacities on the arcs to route the required flow to each sink, that may be an intermediate or a terminal node of an arborescence. Capacity can only be installed in discrete levels, i.e., cables are available only in certain standard capacities. Economies of scale induce the use of a unique higher capacity cable instead of an equivalent set of lower capacity cables to cover the flow requirements of any link. A path from a source to a terminal node requires a lower flow in the measure that we are closer to the terminal node, since many nodes in the path may be intermediate sinks. On the other hand, the reduction of cable capacity levels across any path is inhibited by splicing costs. The objective is to minimize the total cost of the network, given by the sum of the arc capacity (cables) costs plus the splicing costs along the nodes. In addition to the limited supply and the node demand requirements, the model incorporates constraints on the number of cables installed on each edge and the maximum number of splices at each node. The model is a NP-hard combinatorial optimization problem because it is an extension of the Steiner problem in graphs. Moreover, the discrete levels of cable capacity and the need to consider splicing costs increase the complexity of the problem. We include some computational results of the lagrangian heuristics that works well in the practice of computer aided distribution network design.     

A Note on Hitting Maximum and Maximal Cliques With a Stable Set

It was recently proved that any graph satisfying contains a stable set hitting every maximum clique. In this note, we prove that the same is true for graphs satisfying unless the graph is the strong product of an odd hole and .We also provide a counterexample to a recent conjecture on the existence of a stable set hitting every sufficiently large maximal clique.     

实现同一度序列的图的最大团数

图G中最大完全子图的阶数称为G的团效．ω(π)和γ(π)分别表示实现度序列π=(d_1,d_2,…,d_n)的图的最大团数和最小团数．Erds,Jacobson和Lehel开始考虑确定具有相同度序列π的图的可能的团数问题．他们证明了对于充分大的n,有ω(π)-γ(π)-n一2n~(2/3)．在本文中,我们首先估计了一类特殊可图序列的ω(π)之值,其次我们建立了一个估计任意可图序列π的ω(π)之值的算法．     

Heuristics for the Phylogeny Problem

A phylogeny is a tree that relates taxonomic units, based on their similarity over a set of characters. The problem of finding a phylogeny with the minimum number of evolutionary steps (the so-called parsimony criterion) is one of the main problems in comparative biology. In this work, we study different heuristic approaches to the phylogeny problem under the parsimony criterion. New algorithms based on metaheuristics are also proposed. All heuristics are implemented and compared under the same framework, leading to consistent and thorough comparative results. Computational results are reported for benchmark instances from the literature.     

Lagrangian Smoothing Heuristics for Max-Cut

This paper presents a smoothing heuristic for an NP-hard combinatorial problem. Starting with a convex Lagrangian relaxation, a pathfollowing method is applied to obtain good solutions while gradually transforming the relaxed problem into the original problem formulated with an exact penalty function. Starting points are drawn using different sampling techniques that use randomization and eigenvectors. The dual point that defines the convex relaxation is computed via eigenvalue optimization using subgradient techniques. The proposed method turns out to be competitive with the most recent ones. The idea presented here is generic and can be generalized to all box-constrained problems where convex Lagrangian relaxation can be applied. Furthermore, to the best of our knowledge, this is the first time that a Lagrangian heuristic is combined with pathfollowing techniques. The work was supported by the German Research Foundation (DFG) under grant No 421/2-1.     

Using Experimental Design to Find Effective Parameter Settings for Heuristics

In this paper, we propose a procedure, based on statistical design of experiments and gradient descent, that finds effective settings for parameters found in heuristics. We develop our procedure using four experiments. We use our procedure and a small subset of problems to find parameter settings for two new vehicle routing heuristics. We then set the parameters of each heuristic and solve 19 capacity-constrained and 15 capacity-constrained and route-length-constrained vehicle routing problems ranging in size from 50 to 483 customers. We conclude that our procedure is an effective method that deserves serious consideration by both researchers and operations research practitioners.     

非对称距离的旅行商问题的构造算法

章分析了非对称距离的旅行商问题,讨论了节约算法与最小生成树算法两种启发式方法,并用实例进行了说明,最后对算法的有效性进行了说明。     

Greedy Randomized Adaptive Search and Variable Neighbourhood Search for the minimum labelling spanning tree problem

This paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-hard. A Greedy Randomized Adaptive Search Procedure (GRASP) and a Variable Neighbourhood Search (VNS) are proposed in this paper. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics.     

求解基于专用道设置的动态交通规划问题的启发式算法

基于专用道设置的策略,该文提出了一个新的动态交通规划问题。大型运动会要求主办方在规定时间内将指定人员从运动员村运送到指定地点。该问题便是源自2010年广州亚运会的交通需求。其要求在保证30分钟内将运动员从运动员村运送到指定场馆的条件下,最小化设置专用通道的总成本。由于该问题的规模较大,本文提出了三种启发式算法用以求解已提出的线性整数规划模型。计算结果表明,通过该文提出的启发式算法得到的解与相对应的采用数学规划软件Lingo8．0得到的解之间的平均误差均小于1．89％。同时,启发式算法的计算时间远小于Lingo8．0所需的计算时间。     

求解资源约束项目调度问题的启发式算法综述

本文综述了求解RCPSP的启发式算法.首先在对各种优先权规则进行归纳的基础上,概述基于优先权规则的RCPSP启发式算法研究现状;其次,综述项目进度的表述方式及常用超启发式策略,汇总求解RCPSP的超启发式算法的研究成果.此外,简要介绍除上述两大类启发式算法之外的其他几种启发式算法;最后,对全文进行总结,并指出该领域几个有希望的研究方向.