Steiner Tree Problem In Graphs Research Articles

A hybrid genetic algorithm for the steiner tree problem in graphs towards optimizations for wireless sensor networks

A hybrid genetic algorithm for the steiner tree problem in graphs towards optimizations for wireless sensor networks

A heuristic method for solving the Steiner tree problem in graphs using network centralities

We propose a heuristic method of using network centralities for constructing small-weight Steiner trees in this paper. The Steiner tree problem in graphs is one of the practical NP-hard combinatorial optimization problems. Given a graph and a set of vertices called terminals in the graph, the objective of the Steiner tree problem in graphs is to find a minimum weight Steiner tree that is a tree containing all the terminals. Conventional construction methods make a Steiner tree based on the shortest paths between terminals. If these shortest paths are overlapped as much as possible, we can obtain a small-weight Steiner tree. Therefore, we proposed to use network centralities to distinguish which edges should be included to make a small-weight Steiner tree. Experimental results revealed that using the vertex or the edge betweenness centralities contributes to making small-weight Steiner trees.

Solving the Steiner Tree Problem in Graphs with Chaotic Neural Networks

Solving the Steiner Tree Problem in Graphs with Chaotic Neural Networks

Steiner Tree Problem in Graphs and Mixed Integer Linear Programming-Based Approach in GAMS

The Steiner tree problem in graphs involves finding a minimum cost tree which connects a defined subset of the vertices. This problem generalises the minimum spanning tree problem, in contrast, it is NP-complete and is usually solved for large instances by deterministic or stochastic heuristic methods and approximate algorithms. In this paper, however, we focus on a different approach, based on the formulation of a mixed integer programming model and its modification for solving in the professional optimization tool GAMS, which is now capable of solving even large instances of problems of exponential complexity.

Implications, conflicts, and reductions for Steiner trees

The Steiner tree problem in graphs (SPG) is one of the most studied problems in combinatorial optimization. In the past 10 years, there have been significant advances concerning approximation and complexity of the SPG. However, the state of the art in (practical) exact solution of the SPG has remained largely unchallenged for almost 20 years. While the DIMACS Challenge 2014 and the PACE Challenge 2018 brought renewed interest into Steiner tree problems, even the best new SPG solvers cannot match the state of the art on the vast majority of benchmark instances. The following article seeks to advance exact SPG solution once again. The article is based on a combination of three concepts: Implications, conflicts, and reductions. As a result, various new SPG techniques are conceived. Notably, several of the resulting techniques are (provably) stronger than well-known methods from the literature that are used in exact SPG algorithms. Finally, by integrating the new methods into a branch-and-cut framework, we obtain an exact SPG solver that is not only competitive with, but even outperforms the current state of the art on an extensive collection of benchmark sets. Furthermore, we can solve several instances for the first time to optimality.

On the Exact Solution of Prize-Collecting Steiner Tree Problems

The prize-collecting Steiner tree problem (PCSTP) is a well-known generalization of the classic Steiner tree problem in graphs, with a large number of practical applications. It attracted particular interest during the 11th DIMACS Challenge in 2014, and since then, several PCSTP solvers have been introduced in the literature. Although these new solvers further, and often drastically, improved on the results of the DIMACS Challenge, many PCSTP benchmark instances have remained unsolved. The following article describes further advances in the state of the art in exact PCSTP solving. It introduces new techniques and algorithms for PCSTP, involving various new transformations (or reductions) of PCSTP instances to equivalent problems, for example, to decrease the problem size or to obtain a better integer programming formulation. Several of the new techniques and algorithms provably dominate previous approaches. Further theoretical properties of the new components, such as their complexity, are discussed. Also, new complexity results for the exact solution of PCSTP and related problems are described, which form the base of the algorithm design. Finally, the new developments also translate into a strong computational performance: the resulting exact PCSTP solver outperforms all previous approaches, both in terms of runtime and solvability. In particular, it solves several formerly intractable benchmark instances from the 11th DIMACS Challenge to optimality. Moreover, several recently introduced large-scale instances with up to 10 million edges, previously considered to be too large for any exact approach, can now be solved to optimality in less than two hours. Summary of Contribution: The prize-collecting Steiner tree problem (PCSTP) is a well-known generalization of the classic Steiner tree problem in graphs, with many practical applications. The article introduces and analyses new techniques and algorithms for PCSTP that ultimately aim for improved (practical) exact solution. The algorithmic developments are underpinned by results on theoretical aspects, such as fixed-parameter tractability of PCSTP. Computationally, we considerably push the limits of tractibility, being able to solve PCSTP instances with up to 10 million edges. The new solver, which also considerably outperforms the state of the art on smaller instances, will be made publicly available as part of the SCIP Optimization Suite.

Using multiflow formulations to solve the Steiner tree problem in graphs

We present three different mixed integer linear models with a polynomial number of variables and constraints for the Steiner tree problem in graphs. The linear relaxations of these models are compared to show that a good (strong) linear relaxation can be a good approximation for the problem. We present computational results for the STP OR-Library (J.E. Beasley) instances of type b, c, d and e.

Solving Steiner trees: Recent advances, challenges, and perspectives

AbstractThe Steiner tree problem (STP) in graphs is one of the most studied problems in combinatorial optimization. Since its inception in 1970, numerous articles published in the journal Networks have stimulated new theoretical and computational studies on Steiner trees: from approximation algorithms, heuristics, metaheuristics, all the way to exact algorithms based on (mixed) integer linear programming, fixed parameter tractability, or combinatorial branch‐and‐bounds. The pervasive applicability and relevance of Steiner trees have been reinforced by the recent 11th DIMACS Implementation Challenge in 2014 and the PACE 2018 Challenge. This article provides an overview of the rich developments from the last three decades for the STP in graphs and highlights the most recent computational studies for some of its closely related variants.

Optimization of forest road network layout problem

The article describes a three-stage hierarchical method of solving the optimization of forest road network layout problem taking into account road construction and transportation costs using a mathematical model based on the multi-commodity flow model. Optimization of the forest road network layout problem is formulated as an alternative of the Steiner tree problem in graphs. The first stage involves optimization of the forest road network layout within clusters. Many harvesting areas have already been divided into clusters using a clustering optimization model. The second stage involves solution of the similar problem between clusters. At the third stage, the final optimization is performed based on the results obtained at the first two stages. The developed methodology of heuristic solution of optimization of forest road network layout problem allows to improve solving time by 3-6 times with a deviation from the optimal result in the range of 0.5-8%.

Solving the Steiner tree problem in graphs by chaotic search

Solving the Steiner tree problem in graphs by chaotic search

Reduction techniques for the prize collecting Steiner tree problem and the maximum‐weight connected subgraph problem

AbstractThe concept of reduction has frequently distinguished itself as a pivotal ingredient of exact solving approaches for the Steiner tree problem in graphs. In this article we broaden the focus and consider reduction techniques for three Steiner problem variants that have been extensively discussed in the literature and entail various practical applications: The prize‐collecting Steiner tree problem, the rooted prize‐collecting Steiner tree problem and the maximum‐weight connected subgraph problem. By introducing and subsequently deploying numerous new reduction methods, we are able to drastically decrease the size of a large number of benchmark instances, already solving more than 90% of them to optimality. Furthermore, we demonstrate the impact of these techniques on exact solving, using the example of the state‐of‐the‐art Steiner problem solver SCIP‐Jack.

Transformations for the Prize-Collecting Steiner Tree Problem and the Maximum-Weight Connected Subgraph Problem to SAP

Transformations of Steiner tree problem variants have been frequently discussed in the literature. Besides allowing to easily transfer complexity results, they constitute a cen- tral pillar of exact state-of-the-art solvers for well-known variants such as the Steiner tree problem in graphs. In this paper transformations for both the prize-collecting Steiner tree problem and the maximum-weight connected subgraph problem to the Steiner arborescence problem are introduced for the first time. Furthermore, we demonstrate the considerable implications for practical solving approaches, including the computation of strong upper and lower bounds.

Knowledge-guided local search for the prize-collecting Steiner tree problem in graphs

The prize-collecting Steiner tree problem in graphs (PCSPG), as well as its rooted variant (RPCST), are target problems of the 11th DIMACS (the Center for Discrete Mathematics and Theoretical Computer Science) Implementation Challenge held in collaboration with ICERM (the Institute for Computational and Experimental Research in Mathematics). To solve these two problems, this paper proposes a knowledge-guided local search algorithm (K-ILS),11This paper extends [15], which was presented at the workshop of the 11th DIMACS Implementation Challenge, but has not been formally published. which integrates dedicated search strategies and explores structure information of problem instances. K-ILS uses an effective swap-vertex operator for tree transformation associated with a discriminating auxiliary evaluation function as well as several knowledge-guided perturbation strategies. K-ILS additionally employs two new path-based move operators to generate neighboring solutions. The computational results achieved on the benchmark instances of the 11th DIMACS Implementation Challenge using the same computing platform and competition rules demonstrate that K-ILS performs very well compared to the leading algorithms of the challenge. We report additional experiments to analyze the impact of the key components to the performance of the proposed algorithm.

Performance Analysis of Evolutionary Algorithms for Steiner Tree Problems.

The Steiner tree problem (STP) aims to determine some Steiner nodes such that the minimum spanning tree over these Steiner nodes and a given set of special nodes has the minimum weight, which is NP-hard. STP includes several important cases. The Steiner tree problem in graphs (GSTP) is one of them. Many heuristics have been proposed for STP, and some of them have proved to be performance guarantee approximation algorithms for this problem. Since evolutionary algorithms (EAs) are general and popular randomized heuristics, it is significant to investigate the performance of EAs for STP. Several empirical investigations have shown that EAs are efficient for STP. However, up to now, there is no theoretical work on the performance of EAs for STP. In this article, we reveal that the (1+1) EA achieves 3/2-approximation ratio for STP in a special class of quasi-bipartite graphs in expected runtime [Formula: see text], where [Formula: see text], [Formula: see text], and [Formula: see text] are, respectively, the number of Steiner nodes, the number of special nodes, and the largest weight among all edges in the input graph. We also show that the (1+1) EA is better than two other heuristics on two GSTP instances, and the (1+1) EA may be inefficient on a constructed GSTP instance.

SCIP-Jack—a solver for STP and variants with parallelization extensions

The Steiner tree problem in graphs is a classical problem that commonly arises in practical applications as one of many variants. While often a strong relationship between different Steiner tree problem variants can be observed, solution approaches employed so far have been prevalently problem-specific. In contrast, this paper introduces a general-purpose solver that can be used to solve both the classical Steiner tree problem and many of its variants without modification. This versatility is achieved by transforming various problem variants into a general form and solving them by using a state-of-the-art MIP-framework. The result is a high-performance solver that can be employed in massively parallel environments and is capable of solving previously unsolved instances.

A Steiner point candidate-based heuristic framework for the Steiner tree problem in graphs

The underlying models of many practical problems in various engineering fields are equivalent to the Steiner tree problem in graphs, which is a typical NP-hard combinatorial optimization problem. Thus, developing a fast and effective heuristic for the Steiner tree problem in graphs is of universal significance. By analyzing the advantages and disadvantages of the fast classic heuristics, we find that the shortest paths and Steiner points play important roles in solving the Steiner tree problem in graphs. Based on the analyses, we propose a Steiner point candidate-based heuristic algorithm framework (SPCF) for solving the Steiner tree problem in graphs. SPCF consists of four stages: marking [Formula: see text] points, constructing the Steiner tree, eliminating the detour paths, and [Formula: see text]-based refining stage. For each procedure of SPCF, we present several alternative strategies to make the trade-off between the effectiveness and efficiency of the algorithm. By finding the shortest path clusters between vertex sets, several methods are proposed to mark the first type of Steiner point candidates [Formula: see text]. The solution qualities of the classic heuristics are effectively improved by looking [Formula: see text] points as terminals. By constructing a Voronoi diagram, a series of methods are suggested to mark the second type of Steiner point candidates [Formula: see text]. The feasible solution quality is efficiently improved by employing the [Formula: see text] points as the insertable key-vertices in key-vertex insertion local search method. Numerical experiments show that the proposed strategies are all effective for improving the solution quality. Compared with other effective algorithms, the proposed algorithms can achieve better solution quality and speed performance.

A GRASP for the Steiner Tree Problem in Graphs to Support Multicast Routing

In computer networks, multicast routing is that in which a single message is transmitted to multiple recipients from a subset of devices, called multicast group. To address the multicast routing problem, it is usual to model the computer network as a graph. In line with this, some strategies aim at building a routing tree that minimizes the total costs of transmission between the vertices of the routing tree that contains the multicast group, the Steiner tree. However, in large scale applications, local-based strategies may be a better alternative, since they can also be easily adapted to tackle distributed systems. In the literature, to the extent of our knowledge, to construct approximate Steiner tree using this type of approach remains a challenge. In this paper, we present a neighborhood-based metaheuristic to find an approximate solution for the Steiner tree in graphs. In the computational experiments, we used benchmark instances of small, medium and large scales and compared the results achieved by the proposed strategy with the best found so far. In addition, we analyze a real word instance for which the proposed algorithm has obtained good performance.

Key-node-based local search discrete artificial bee colony algorithm for obstacle-avoiding rectilinear Steiner tree construction

The obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) problem is a fundamental problem in very large-scale integrated circuit physical design and can be reduced to the Steiner tree problem in graphs (GSTP), which can be solved by using three types of common methods: classic heuristics, local search algorithms, or computational intelligence algorithms. However, classic heuristics have poor solution qualities; local search algorithms easily fall into the problem of the local optimum; and the searching effects of the existing computational intelligence algorithms are poor for this problem. In order to improve the solution quality, we propose a novel discrete artificial bee colony algorithm for constructing an obstacle-avoiding rectilinear Steiner tree. We first generate the escape graph for the OARSMT problem. Then, we search for a near-optimal solution consisting of some edges of escape graph by using the discrete ABC algorithm. We apply a key-node neighborhood configuration for the local search strategy and introduce two local search operators. We then naturally use a key-node-based encoding scheme for representing the feasible solution and obtain a tight searching scope. We employ a modified classic heuristic as the encoder that can produce a feasible solution. Experiments conducted on both general GSTP and very large-scale integrated circuit instances reveal the superior performance of the proposed method in terms of the solution quality among the state-of-the-art algorithms.

A New Hybrid GRASP with the Pilot Method Forthe Delay-Constrained Multicast Routing Problem

Multicast routing problem is a well know optimization problem for transmitting real-time multimedia applications in telecommunication networks. As the underpinning mathematical model, the constrained minimum Steiner tree problem in graphs is a well-known NP-complete problem. In this paper we investigate a new hybrid GRASP (Greedy Randomized Adaptive Search Procedure) approach where a pilot method is applied to further enhance the search for the Delay-Constrained Least-Cost (DCLC) multicast routing problem. Experimental results demonstrate the efficiency of the hybrid GRASP algorithm and the contributions of the post-processing pilot method to better solutions in most cases. The proposed GRASP approach is a competitive approach in solving the DCLC multicast routing problem.

Linking like with like: optimising connectivity between environmentally-similar habitats

Habitat fragmentation is one of the greatest threats to biodiversity. To minimise the effect of fragmentation on biodiversity, connectivity between otherwise isolated habitats should be promoted. However, the identification of linkages favouring connectivity is not trivial. Firstly, they compete with other land uses, so they need to be cost-efficient. Secondly, linkages for one species might be barriers for others, so they should effectively account for distinct mobility requirements. Thirdly, detailed information on the auto-ecology of most of the species is lacking, so linkages need being defined based on surrogates. In order to address these challenges we develop a framework that (a) identifies environmentally-similar habitats; (b) identifies environmental barriers (i.e., regions with a very distinct environment from the areas to be linked), and; (c) determines cost-efficient linkages between environmentally-similar habitats, free from environmental barriers. The assumption is that species with similar ecological requirements occupy the same environments, so environmental similarity provides a rationale for the identification of the areas that need to be linked. A variant of the classical minimum Steiner tree problem in graphs is used to address c). We present a heuristic for this problem that is capable of handling large datasets. To illustrate the framework we identify linkages between environmentally-similar protected areas in the Iberian Peninsula. The Natura 2000 network is used as a positive ‘attractor’ of links while the human footprint is used as ‘repellent’ of links. We compare the outcomes of our approach with cost-efficient networks linking protected areas that disregard the effect of environmental barriers. As expected, the latter achieved a smaller area covered with linkages, but with barriers that can significantly reduce the permeability of the landscape for the dispersal of some species.