Tabu Search for a Network Loading Problem with Multiple Facilities

This paper examines a network design problem that arises in the telecommunications industry. In this problem, communication between a gateway vertex and a number of demand vertices is achieved through a network of fiber optic cables. Since each cable has an associated capacity (bandwidth), enough capacity must be installed on the links of the network to satisfy the demand, using possibly different types of cables. Starting with a network with no capacity or some capacity already installed, a tabu search heuristic is designed to find a solution that minimizes the cost of installing any additional capacity on the network. This tabu search applies a k-shortest path algorithm to find alternative paths from the gateway to the demand vertices. Numerical results are presented on different types of networks with up to 200 vertices and 100 demand vertices.     

Network flow interdiction on planar graphs

The network flow interdiction problem asks to reduce the value of a maximum flow in a given network as much as possible by removing arcs and vertices of the network constrained to a fixed budget. Although the network flow interdiction problem is strongly NP-complete on general networks, pseudo-polynomial algorithms were found for planar networks with a single source and a single sink and without the possibility to remove vertices. In this work, we introduce pseudo-polynomial algorithms that overcome various restrictions of previous methods. In particular, we propose a planarity-preserving transformation that enables incorporation of vertex removals and vertex capacities in pseudo-polynomial interdiction algorithms for planar graphs. Additionally, a new approach is introduced that allows us to determine in pseudo-polynomial time the minimum interdiction budget needed to remove arcs and vertices of a given network such that the demands of the sink node cannot be completely satisfied anymore. The algorithm works on planar networks with multiple sources and sinks satisfying that the sum of the supplies at the sources equals the sum of the demands at the sinks. A simple extension of the proposed method allows us to broaden its applicability to solve network flow interdiction problems on planar networks with a single source and sink having no restrictions on the demand and supply. The proposed method can therefore solve a wider class of flow interdiction problems in pseudo-polynomial time than previous pseudo-polynomial algorithms and is the first pseudo-polynomial algorithm that can solve non-trivial planar flow interdiction problems with multiple sources and sinks. Furthermore, we show that the k-densest subgraph problem on planar graphs can be reduced to a network flow interdiction problem on a planar graph with multiple sources and sinks and polynomially bounded input numbers.     

Approximating the maximum vertex/edge weighted clique using local search

This paper extends the recently introduced Phased Local Search (PLS) algorithm to more difficult maximum clique problems and also adapts the algorithm to handle maximum vertex/edge weighted clique instances. PLS is a stochastic reactive dynamic local search algorithm that interleaves sub-algorithms which alternate between sequences of iterative improvement, during which suitable vertices are added to the current sub-graph, and plateau search, where vertices of the current sub-graph are swapped with vertices not contained in the current sub-graph. These sub-algorithms differ in firstly their vertex selection techniques in that selection can be solely based on randomly selecting a vertex, randomly selecting within highest vertex degree, or random selecting within vertex penalties that are dynamically adjusted during the search. Secondly, the perturbation mechanism used to overcome search stagnation differs between the sub-algorithms. PLS has no problem instance dependent parameters and achieves state-of-the-art performance for maximum clique and maximum vertex/edge weighted clique problems over a large range of the commonly used DIMACS benchmark instances.     

Applying local search to the feedback vertex set problem

The feedback vertex set problem (FVSP) consists in making a given directed graph acyclic by removing as few vertices as possible. In spite of the importance of this NP-hard problem, no local search approach had been proposed so far for tackling it. Building on a property of acyclic graphs, we suggest in this paper a new representation of the solutions of the FVSP (feedback sets). Thanks to this solution representation, we are able to design a local transformation (equivalent to a neighborhood) that changes a feedback set into a new one. Based on this neighborhood, we have developed a simulated annealing algorithm for the FVSP. Our experiments show that our algorithm outperforms the best existing heuristic, namely the greedy adaptive search procedure by Pardalos et al.     

The Attractive Traveling Salesman Problem

In the Attractive Traveling Salesman Problem the vertex set is partitioned into facility vertices and customer vertices. A maximum profit tour must be constructed on a subset of the facility vertices. Profit is computed through an attraction function: every visited facility vertex attracts a portion of the profit from the customer vertices based on the distance between the facility and customer vertices, and the attractiveness of the facility vertex. A gravity model is used for computing the profit attraction. The problem is formulated as an integer non-linear program. A linearization is proposed and strengthened through the introduction of valid inequalities, and a branch-and-cut algorithm is developed. A tabu search algorithm is also implemented. Computational results are reported.     

Heuristics for multiple facility location in a network using the variance criterion

In cyclic networks the p-variance location problem is NP-hard, and therefore it is suitable to use heuristic methods to find approximate solutions to the problem. To this end, two strategies are explored, the first using combinatorial search procedures over the vertex set, whereas the second searches for the solution over the entire network. The initial vertex set solutions are generated by using tabu search, variable neighbourhood search and interchange procedures. The heuristics have been tested on instances of up to 30 vertices and 70 edges, and their performances compared.     

Hybridized evolutionary local search algorithm for the team orienteering problem with time windows

The orienteering problem (OP) consists in finding an elementary path over a subset of vertices. Each vertex is associated with a profit that is collected on the visitor’s first visit. The objective is to maximize the collected profit with respect to a limit on the path’s length. The team orienteering problem (TOP) is an extension of the OP where a fixed number m of paths must be determined. This paper presents an effective hybrid metaheuristic to solve both the OP and the TOP with time windows. The method combines the greedy randomized adaptive search procedure (GRASP) with the evolutionary local search (ELS). ELS generates multiple distinct child solutions using a mutation mechanism. Each child solution is further improved by a local search procedure. GRASP provides multiple starting solutions to the ELS. The method is able to improve several best known results on available benchmark instances.     

Optimally reliable networks

In this paper, we consider the probability of disconnection of a graph as a measure of network reliability. We compare the vertex and edge failure cases, and then concentrate on the vertex failure case. Optimal graphs are graphs which minimise the probability of disconnection for a given number of vertices and edges when the probability of vertex failure is small. We describe the known results on the construction of optimal regular graphs and present some new results on the construction of optimal nonregular graphs.     

Distributed Online Frequency Assignment in Cellular Networks

A cellular network is generally modeled as a subgraph of the triangular lattice. The distributed online frequency assignment problem can be abstracted as a multicoloring problem on a weighted graph, where the weight vector associated with the vertices models the number of calls to be served at the vertices and is assumed to change over time. In this paper, we develop a framework for studying distributed online frequency assignment in cellular networks. We present the first distributed online algorithms for this problem with proven bounds on their competitive ratios. We show a series of algorithms that use at each vertex information about increasingly larger neighborhoods of the vertex, and that achieve better competitive ratios. In contrast, we show lower bounds on the competitive ratios of some natural classes of online algorithms.     

Optimisation of unweighted/weighted maximum independent sets and minimum vertex covers

This paper extends the recently introduced Phased Local Search (PLS) maximum clique algorithm to unweighted/weighted maximum independent set and minimum vertex cover problems. PLS is a stochastic reactive dynamic local search algorithm that interleaves sub-algorithms which alternate between sequences of iterative improvement, during which suitable vertices are added to the current sub-graph, and plateau search, during which vertices of the current sub-graph are swapped with vertices not contained in the current sub-graph. These sub-algorithms differ in their vertex selection techniques and also in the perturbation mechanism used to overcome search stagnation. PLS has no problem instance dependent parameters and achieves state-of-the-art performance over a large range of the commonly used DIMACS and other benchmark instances.     

Fast local search for the maximum independent set problem

Given a graph G=(V,E), the independent set problem is that of finding a maximum-cardinality subset S of V such that no two vertices in S are adjacent. We introduce two fast local search routines for this problem. The first can determine in linear time whether a maximal solution can be improved by replacing a single vertex with two others. The second routine can determine in O(mΔ) time (where Δ is the highest degree in the graph) whether there are two solution vertices than can be replaced by a set of three. We also present a more elaborate heuristic that successfully applies local search to find near-optimum solutions to a wide variety of instances. We test our algorithms on instances from the literature as well as on new ones proposed in this paper.     

J-means and I-means for minimum sum-of-squares clustering on networks

Given a graph, the Edge minimum sum-of-squares clustering problem requires finding p prototypes (cluster centres) by minimizing the sum of their squared distances from a set of vertices to their nearest prototype, where a prototype can be either a vertex or an inner point of an edge. In this paper we have implemented Variable neighborhood search based heuristic for solving it. We consider three different local search procedures, K-means, J-means, and a new I-means heuristic. Experimental results indicate that the implemented VNS-based heuristic produces the best known results in the literature.     

The cover pebbling number of graphs

A pebbling move on a graph consists of taking two pebbles off of one vertex and placing one pebble on an adjacent vertex. In the traditional pebbling problem we try to reach a specified vertex of the graph by a sequence of pebbling moves. In this paper we investigate the case when every vertex of the graph must end up with at least one pebble after a series of pebbling moves. The cover pebbling number of a graph is the minimum number of pebbles such that however the pebbles are initially placed on the vertices of the graph we can eventually put a pebble on every vertex simultaneously. We find the cover pebbling numbers of trees and some other graphs. We also consider the more general problem where (possibly different) given numbers of pebbles are required for the vertices.     

A variable neighborhood-based heuristic for the heterogeneous fleet vehicle routing problem

The heterogeneous fleet vehicle routing problem is investigated using some adaptations of the variable neighborhood search (VNS). The initial solution is obtained by Dijkstra’s algorithm based on a cost network constructed by the sweep algorithm and the 2-opt. Our VNS algorithm uses several neighborhoods which are adapted for this problem. In addition, a number of local search methods together with a diversification procedure are used. Two VNS variants, which differ in the order the diversification and Dijkstra’s algorithm are used, are implemented. Both variants appear to be competitive and produce new best results when tested on the data sets from the literature. We also constructed larger data sets for which benchmarking results are provided for future comparison.     

An Ant Colony Optimization Algorithm for the Minimum Weight Vertex Cover Problem

Given an undirected graph and a weighting function defined on the vertex set, the minimum weight vertex cover problem is to find a vertex subset whose total weight is minimum subject to the premise that the selected vertices cover all edges in the graph. In this paper, we introduce a meta-heuristic based upon the Ant Colony Optimization (ACO) approach, to find approximate solutions to the minimum weight vertex cover problem. In the literature, the ACO approach has been successfully applied to several well-known combinatorial optimization problems whose solutions might be in the form of paths on the associated graphs. A solution to the minimum weight vertex cover problem however needs not to constitute a path. The ACO algorithm proposed in this paper incorporates several new features so as to select vertices out of the vertex set whereas the total weight can be minimized as much as possible. Computational experiments are designed and conducted to study the performance of our proposed approach. Numerical results evince that the ACO algorithm demonstrates significant effectiveness and robustness in solving the minimum weight vertex cover problem.     

Foolproof eternal domination in the all-guards move model

The eternal domination problem requires a graph be protected against an infinitely long sequence of attacks at vertices, by guards located at vertices, with the requirement that the configuration of guards induces a dominating set at all times. An attack is defended by sending a guard from a neighboring vertex to the attacked vertex. We allow all guards to move to neighboring vertices in response to an attack, but allow the attacked vertex to choose which neighboring guard moves to the attacked vertex. This is the all-guards move version of the ??foolproof?? eternal domination problem that has been previously studied. We present some results and conjectures on this problem.     

Embedding a novel objective function in a two-phased local search for robust vertex coloring

In this paper, we propose a two-phased local search for vertex coloring. The algorithm alternately executes two closely interacting functionalities, i.e., a stochastic and a deterministic local search. The stochastic phase is basically based on biased random sampling that, according to a probability matrix storing the probability a vertex can be assigned to a color, iteratively constructs feasible colorings. The deterministic phase, instead, consists in assigning sequentially, according to a given ordering, each vertex to the color which causes the lowest increase of the solution penalty, and then, when the schedule is constructed, swap operations are executed to improve the performance. The interaction between the two phases is implemented by tunnelling information of what happened during a phase to the successive ones. Beyond the algorithm scheme, the novelty of the approach stems from the fact that the objective function is not minimizing the number of colors but a new penalty function. The proposed approach is tested on known benchmarks for the studied problem available on the public domain. From a comparison to the state of the art it appears that the proposed approach is robust and is able to achieve best known results.     

Eulerian detachments with local edge-connectivity

For a graph G, a detachment operation at a vertex transforms the graph into a new graph by splitting the vertex into several vertices in such a way that the original graph can be obtained by contracting all the split vertices into a single vertex. A graph obtained from a given graph G by applying detachment operations at several vertices is called a detachment of graph G. While detachment operations may decrease the connectivity of graphs, there are several works on conditions for preserving the connectivity. In this paper, we present necessary and sufficient conditions for a given graph/digraph to have an Eulerian detachment that satisfies a given local edge-connectivity requirement. We also discuss conditions for the detachment to be loopless.     

An exact algorithm and a metaheuristic for the multi-vehicle covering tour problem with a constraint on the number of vertices

The multi-vehicle covering tour problem (m-CTP) involves finding a minimum-length set of vehicle routes passing through a subset of vertices, subject to constraints on the length of each route and the number of vertices that it contains, such that each vertex not included in any route lies within a given distance of a route. This paper tackles a particular case of m-CTP where only the restriction on the number of vertices is considered, i.e., the constraint on the length is relaxed. The problem is solved by a branch-and-cut algorithm and a metaheuristic. To develop the branch-and-cut algorithm, we use a new integer programming formulation based on a two-commodity flow model. The metaheuristic is based on the evolutionary local search (ELS) method proposed in [23]. Computational results are reported for a set of test problems derived from the TSPLIB.     

A new memetic algorithm based on cellular learning automata for solving the vertex coloring problem

Vertex coloring problem is a combinatorial optimization problem in graph theory in which a color is assigned to each vertex of graph such that no two adjacent vertices have the same color. In this paper a new hybrid algorithm which is obtained from combination of cellular learning automata (CLA) and memetic algorithm (MA) is proposed for solving the vertex coloring problem. CLA is an effective probabilistic learning model combining cellular automata and learning automaton (LA). Irregular CLA (ICLA) is a generalization of CLA in which the restriction of rectangular grid structure in CLA is removed. The proposed algorithm is based on the irregular open CLA (IOCLA) that is an extension of ICLA in which the evolution of CLA is influenced by both local and global environments. Similar to other IOCLA-based algorithms, in the proposed algorithm, local environment is constituted by neighboring LAs of any cell and the global environment consists of a pool of memes in which each meme corresponds to a certain local search method. Each meme is represented by a set of LAs from which the history of the corresponding local search method can be extracted. To show the superiority of the proposed algorithm over some well-known algorithms, several computer experiments have been conducted. The results show that the proposed algorithm performs better than other methods in terms of running time of algorithm and the required number of colors.