A tabu search procedure based on a random Roulette diversification for the weighted maximal planar graph problem

An efficient and effective tabu search implementation for the weighted maximal planar graph problem is proposed. The search incorporates a number of novel features including: the introduction of a new set of two-move operators; a move-cache-memory structure for efficiently searching the neighbourhood space; and a random roulette selection from a ranked list of best moves for diversification. The effects of these and other features on solution quality are investigated. Computational results are reported on a set of 100 benchmark instances of sizes varying from 20 to 100 vertices. The results demonstrate that the new probabilistic tabu search algorithm is very competitive with state of the art algorithms in the literature in terms of both solution quality and computational effort.Scope     

A parallel adaptive tabu search approach

This paper presents a new approach for parallel tabu search based on adaptive parallelism. Adaptive parallelism was used to dynamically adjust the parallelism degree of the application with respect to the system load. Adaptive parallelism demonstrates that high-performance computing using a hundred of heterogeneous workstations combined with massively parallel machines is feasible to solve large optimization problems. The parallel tabu search algorithm includes different tabu list sizes and new intensification/diversification mechanisms. Encouraging results have been obtained in solving the quadratic assignment problem. We have improved the best known solutions for some large real-world problems.     

A layout algorithm for undirected compound graphs

We present an algorithm for the layout of undirected compound graphs, relaxing restrictions of previously known algorithms in regards to topology and geometry. The algorithm is based on the traditional force-directed layout scheme with extensions to handle multi-level nesting, edges between nodes of arbitrary nesting levels, varying node sizes, and other possible application-specific constraints. Experimental results show that the execution time and quality of the produced drawings with respect to commonly accepted layout criteria are quite satisfactory. The algorithm has also been successfully implemented as part of a pathway integration and analysis toolkit named PATIKA, for drawing complicated biological pathways with compartmental constraints and arbitrary nesting relations to represent molecular complexes and various types of pathway abstractions.     

A tabu search approach for the minimum sum-of-squares clustering problem

In this paper, a tabu search based clustering approach called TS-Clustering is proposed to deal with the minimum sum-of-squares clustering problem. In the TS-Clustering algorithm, five improvement operations and three neighborhood modes are given. The improvement operation is used to enhance the clustering solution obtained in the process of iterations, and the neighborhood mode is used to create the neighborhood of tabu search. The superiority of the proposed method over some known clustering techniques is demonstrated for artificial and real life data sets.     

A graph coloring heuristic using partial solutions and a reactive tabu scheme

Most of the recent heuristics for the graph coloring problem start from an infeasible k-coloring (adjacent vertices may have the same color) and try to make the solution feasible through a sequence of color exchanges. In contrast, our approach (called FOO-PARTIALCOL), which is based on tabu search, considers feasible but partial solutions and tries to increase the size of the current partial solution. A solution consists of k disjoint stable sets (and, therefore, is a feasible, partial k-coloring) and a set of uncolored vertices. We introduce a reactive tabu tenure which substantially enhances the performance of both our heuristic as well as the classical tabu algorithm (called TABUCOL) proposed by Hertz and de Werra [Using tabu search techniques for graph coloring, Computing 1987;39:345–51]. We will report numerical results on different benchmark graphs and we will observe that FOO-PARTIALCOL, though very simple, outperforms TABUCOL on some instances, provides very competitive results on a set of benchmark graphs which are known to be difficult, and outperforms the best-known methods on the graph flat300_28_0. For this graph, FOO-PARTIALCOL finds an optimal coloring with 28 colors. The best coloring achieved to date uses 31 colors. Algorithms very close to TABUCOL are still used as intensification procedures in the best coloring methods, which are evolutionary heuristics. FOO-PARTIALCOL could then be a powerful alternative. In conclusion FOO-PARTIALCOL is one of the most efficient simple local search coloring methods yet available.     

A tabu search approach for scheduling hazmat shipments

Vehicle routing and scheduling are two main issues in the hazardous material (hazmat) transportation problem. In this paper, we study the problem of managing a set of hazmat transportation requests in terms of hazmat shipment route selection and actual departure time definition. For each hazmat shipment, a set of minimum and equitable risk alternative routes from origin to destination points and a preferred departure time are given. The aim is to assign a route to each hazmat shipment and schedule these shipments on the assigned routes in order to minimize the total shipment delay, while equitably spreading the risk spatially and preventing the risk induced by vehicles traveling too close to each other. We model this hazmat shipment scheduling problem as a job-shop scheduling problem with alternative routes. No-wait constraints arise in the scheduling model as well, since, supposing that no safe area is available, when a hazmat vehicle starts traveling from the given origin it cannot stop until it arrives at the given destination. A tabu search algorithm is proposed for the problem, which is experimentally evaluated on a set of realistic test problems over a regional area, evaluating the provided solutions also with respect to the total route risk and length.     

A tabu search approach to an urban transport problem in northern Spain

In this work we analyze an urban transport problem that the City Council of Burgos, a city in northern Spain, has posed to the authors. Given a fleet of buses and drivers, the problem consists in designing routes and assigning buses to the routes such that the service level is optimized. The optimality of the service level is measured in terms of the waiting time at the bus stops and the duration of the trip. Thus, the problem comprises two decision levels (route design and bus assignment) and differs from other urban transport models found in the literature. In order to solve the problem, we propose two algorithms: one with a local search strategy and another with a tabu search strategy. In both cases, the solutions of the two decision levels are modified in alternating steps. The proposed algorithms obtained significantly better results than the tools currently applied by the transport authorities. In addition, the solutions obtained are very robust with respect to variations on demand, as shown by the experiments.     

A novel hybrid tabu search approach to container loading

The container loading problem, which is significant for a number of industrial sectors, aims to obtain a high space utilisation in the container while satisfying practical constraints. This paper presents a novel hybrid tabu search approach to the container loading problem. A loading heuristic is devised to incorporate heuristic strategies with a handling method for remaining spaces to generate optimal loading arrangements of boxes with stability considered. The tabu search technique, which covers the encoding, evaluation criteria and configuration of neighbourhood and candidate solutions, is used to improve the performance of the loading heuristic. Experimental results with benchmark data show that the hybrid approach provides a better space utilisation than the published approaches under the condition of all loaded boxes with one hundred percent support from below. Moreover, it is shown that the hybrid tabu search can solve problems with the constraints of weight limit and weight distribution with real world data.     

A tabu search based hybrid evolutionary algorithm for the max-cut problem

This paper presents a tabu search based hybrid evolutionary algorithm (TSHEA) for solving the max-cut problem. The proposed algorithm integrates a distance-and-quality based solution combination operator and a tabu search procedure based on neighborhood combination of one-flip and constrained exchange moves. Comparisons with leading reference algorithms from the literature disclose that the proposed algorithm discovers new best solutions for 15 out of 91 instances, while matching the best known solutions on all but 4 instances. Analysis indicates that the neighborhood combination and the solution combination operator play key roles to the effectiveness of the proposed algorithm.     

A tabu search based memetic algorithm for the max-mean dispersion problem

Given a set V of n elements and a distance matrix ${[d_{\mathit{ij}}]}_{n\times n}$ among elements, the max-mean dispersion problem (MaxMeanDP) consists in selecting a subset M from V such that the mean dispersion (or distance) among the selected elements is maximized. Being a useful model to formulate several relevant applications, MaxMeanDP is known to be NP-hard and thus computationally difficult. In this paper, we present a tabu search based memetic algorithm for MaxMeanDP which relies on solution recombination and local optimization to find high quality solutions. One key contribution is the identification of the fast neighborhood induced by the one-flip operator which takes linear time. Computational experiments on the set of 160 benchmark instances with up to 1000 elements commonly used in the literature show that the proposed algorithm improves or matches the published best known results for all instances in a short computing time, with only one exception, while achieving a high success rate of 100%. In particular, we improve 53 previous best results (new lower bounds) out of the 60 most challenging instances. Results on a set of 40 new large instances with 3000 and 5000 elements are also presented. The key ingredients of the proposed algorithm are investigated to shed light on how they affect the performance of the algorithm.     

A layout algorithm for signaling pathways

Visualization is crucial to the effective analysis of biological pathways. A poorly laid out pathway confuses the user, while a well laid out one improves the user’s comprehension of the underlying biological phenomenon.We present a new, elegant algorithm for layout of biological signaling pathways. Our algorithm uses a force-directed layout scheme, taking into account directional and rectangular regional constraints enforced by different molecular interaction types and subcellular locations in a cell. The algorithm has been successfully implemented as part of a pathway visualization and analysis toolkit named Patika, and results with respect to computational complexity and quality of the layout have been found satisfactory. The algorithm may be easily adapted to be used in other applications with similar conventions and constraints as well.Patika version 1.0 beta is available upon request at http://www.patika.org.     

一种基于禁忌搜索的RFID读写器部署算法*

为解决射频识别（RFID）读写器部署问题,提出了一种基于禁忌搜索算法的读写器部署方法。在增加了读写器容量约束的读写器部署模型基础上,采用禁忌搜索算法来寻找最优部署方案。通过仿真可以得出该算法性能稳定。与遗传算法的数据对比,提出的基于禁忌搜索的读写器部署方法在覆盖率大于遗传算法的同时,迭代次数小于遗传算法。     

A graph grammar‐based approach for graph layout

As a two‐dimensional formal tool, graph grammars are capable of handling the layout problems of visual programming languages. Based on an edge‐based graph grammar (EGG), this paper proposes a novel layout approach that uses the unique features of EGG and overcomes the weakness of existing layout approaches. In order to make the approach rigorous yet concise, the graph grammar mechanisms with layout constraints and quantitative analysis techniques are combined together as an integrity. First, the basic notions of EGG are briefly introduced; second, the layout approach is presented that consists of two phases, ie, bottom‐up parsing and top‐down derivation. Finally, a case study is given by taking the standard flowchart as an example to demonstrate the working process of the proposed approach.     

On transitions in the behaviour of tabu search algorithm TabuCol for graph colouring

Even though tabu search is one of the most popular metaheuristic search strategies, its understanding in terms of behavioural transitions and parameter tuning is still very limited. In this paper, we present a theoretical and experimental study of a popular tabu search algorithm TabuCol for graph colouring. We show that for some instances, there are sharp transitions in the behaviour of TabuCol, depending on the value of tabu tenure parameter. The location of this transition depends on graph structure and may also depend on its size. This is further supported by an experimental study of success rate profiles, which we define as an empirical measure of these transitions. We study the success rate profiles for a range of graph colouring instances, from 2-colouring of trees and forests to several instances from the DIMACS benchmark. These reveal that TabuCol may exhibit a spectrum of different behaviours ranging from simple transitions to highly complex probabilistic behaviour.     

An effective multilevel tabu search approach for balanced graph partitioning

Graph partitioning is one of the fundamental NP-complete problems which is widely applied in many domains, such as VLSI design, image segmentation, data mining, etc. Given a graph G=(V,E), the balanced k-partitioning problem consists in partitioning the vertex set V into k disjoint subsets of about the same size, such that the number of cutting edges is minimized. In this paper, we present a multilevel algorithm for balanced partition, which integrates a powerful refinement procedure based on tabu search with periodic perturbations. Experimental evaluations on a wide collection of benchmark graphs show that the proposed approach not only competes very favorably with the two well-known partitioning packages METIS and CHACO, but also improves more than two thirds of the best balanced partitions ever reported in the literature.     

A tabu search approach for the livestock collection problem

We present a problem dealing with transportation of live animals to slaughterhouses. The problem is taken from the Norwegian meat industry, and may be viewed as a vehicle routing problem extended with inventory constraints to ensure a smooth production flow at the slaughterhouse. In addition, several constraints to ensure animal welfare have to be met. These include duration limits for how long animals can stay on the vehicle as well as rules for mixing different animal categories. In this paper, we show that this real-world problem can be solved heuristically, even if it is large both in size and complexity. A tabu search based solution method for the problem is presented, and results from computational testing are given, including comparisons with manual solutions from today's planning system.     

A deterministic tabu search algorithm for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is a difficult optimisation problem in vehicle routing with applications where a service must be provided by a set of vehicles on specified roads. A heuristic algorithm based on tabu search is proposed and tested on various sets of benchmark instances. The computational results show that the proposed algorithm produces high quality results within a reasonable computing time. Some new best solutions are reported for a set of test problems used in the literature.     

A tabu search algorithm for structural software testing

This paper presents a tabu search metaheuristic algorithm for the automatic generation of structural software tests. It is a novel work since tabu search is applied to the automation of the test generation task, whereas previous works have used other techniques such as genetic algorithms. The developed test generator has a cost function for intensifying the search and another for diversifying the search that is used when the intensification is not successful. It also combines the use of memory with a backtracking process to avoid getting stuck in local minima. Evaluation of the generator was performed using complex programs under test and large ranges for input variables. Results show that the developed generator is both effective and efficient.     

GO: A cluster algorithm for graph visualization

As we are in the big data age, graph data such as user networks in Facebook and Flickr becomes large. How to reduce the visual complexity of a graph layout is a challenging problem. Clustering graphs is regarded as one of effective ways to address this problem. Most of current graph visualization systems, however, directly use existing clustering algorithms that are not originally developed for the visualization purpose. For graph visualization, a clustering algorithm should meet specific requirements such as the sufficient size of clusters, and automatic determination of the number of clusters. After identifying the requirements of clustering graphs for visualization, in this paper we present a new clustering algorithm that is particularly designed for visualization so as to reduce the visual complexity of a layout, together with a strategy for improving the scalability of our algorithm. Experiments have demonstrated that our proposed algorithm is capable of detecting clusters in a way that is required in graph visualization.