A comprehensive comparison of metaheuristics for the repetition-free longest common subsequence problem

This paper deals with an NP-hard string problem from the bio-informatics field: the repetition-free longest common subsequence problem. This problem has enjoyed an increasing interest in recent years, which has resulted in the application of several pure as well as hybrid metaheuristics. However, the literature lacks a comprehensive comparison between those approaches. Moreover, it has been shown that general purpose integer linear programming solvers are very efficient for solving many of the problem instances that were used so far in the literature. Therefore, in this work we extend the available benchmark set, adding larger instances to which integer linear programming solvers cannot be applied anymore. Moreover, we provide a comprehensive comparison of the approaches found in the literature. Based on the results we propose a hybrid between two of the best methods which turns out to inherit the complementary strengths of both methods.Peer ReviewedPostprint (author's final draft

Highway construction for wireless sensor networks

Wireless Sensor Networks are a rapidly growing field of study with many open research topics. The aim of this project is to build a hierarchy of clusters in wireless sensor networks and to communicate them through distinguished paths. Those paths are known as highways, and simplify higher level node inter-communication while reducing energy and memory requirements. To achieve this goal several distributed algorithms were designed and tested either in simulators or in real hardware. The message delivery rate, through highways, measured in hardware was close to 70% and it effectively served as base for a higher level network module to make end to end communication between every node of the connected network. This opens a way for the development of more algorithms to make Wireless Sensor Networks communications on large deployments effective and troubleless.Postprint (published version

A hybrid evolutionary algorithm based on solution merging for the longest arc-preserving common subsequence problem

The longest arc-preserving common subsequence problem is an NP-hard combinatorial optimization problem from the field of computational biology. This problem finds applications, in particular, in the comparison of art-annotated ribonucleic acid (RNA) sequences. In this work we propose a simple, hybrid evolutionary algorithm to tackle this problem. The most important feature of this algorithm concerns a crossover operator based on solution merging. In solution merging, two or more solutions to the problem are merged, and an exact technique is used to find the best solution within this union. It is experimentally shown that the proposed algorithm outperforms a heuristic from the literature.Peer ReviewedPostprint (author's final draft

Hybrid techniques based on solving reduced problem instances for a longest common subsequence problem

Finding the longest common subsequence of a given set of input strings is a relevant problem arising in various practical settings. One of these problems is the so-called longest arc-preserving common subsequence problem. This NP-hard combinatorial optimization problem was introduced for the comparison of arc-annotated ribonucleic acid (RNA) sequences. In this work we present an integer linear programming (ILP) formulation of the problem. As even in the context of rather small problem instances the application of a general purpose ILP solver is not viable due to the size of the model, we study alternative ways based on model reduction in order to take profit from this ILP model. First, we present a heuristic way for reducing the model, with the subsequent application of an ILP solver. Second, we propose the application of an iterative hybrid algorithm that makes use of an ILP solver for generating high quality solutions at each iteration. Experimental results concerning artificial and real problem instances show that the proposed techniques outperform an available technique from the literature.Peer ReviewedPostprint (author's final draft

Computación en Internet: librería MALLBA para problemas de optimización.

Postprint (published version

Large neighborhood search for the most strings with few bad columns problem

In this work, we consider the following NP-hard combinatorial optimization problem from computational biology. Given a set of input strings of equal length, the goal is to identify a maximum cardinality subset of strings that differ maximally in a pre-defined number of positions. First of all, we introduce an integer linear programming model for this problem. Second, two variants of a rather simple greedy strategy are proposed. Finally, a large neighborhood search algorithm is presented. A comprehensive experimental comparison among the proposed techniques shows, first, that larger neighborhood search generally outperforms both greedy strategies. Second, while large neighborhood search shows to be competitive with the stand-alone application of CPLEX for small- and medium-sized problem instances, it outperforms CPLEX in the context of larger instances.Peer ReviewedPostprint (author's final draft

AntNetAlign: Ant colony optimization for network alignment

The code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean.com/). More information on the Reproducibility Badge Initiative is available at https://www.elsevier.com/physical-sciences-andengineering/computer-science/journalsNetwork Alignment (NA) is a hard optimization problem with important applications such as, for example, the identification of orthologous relationships between different proteins and of phylogenetic relationships between species. Given two (or more) networks, the goal is to find an alignment between them, that is, a mapping between their respective nodes such that the topological and functional structure is well preserved. Although the problem has received great interest in recent years, there is still a need to unify the different trends that have emerged from diverse research areas. In this paper, we introduce AntNetAlign, an Ant Colony Optimization (ACO) approach for solving the problem. The proposed approach makes use of similarity information extracted from the input networks to guide the construction process. Combined with an improvement measure that depends on the current construction state, it is able to optimize any of the three main topological quality measures. We provide an extensive experimental evaluation using real-world instances that range from Protein–Protein Interaction (PPI) networks to Social Networks. Results show that our method outperforms other state-of-the-art approaches in two out of three of the tested scores within a reasonable amount of time, specially in the important score. Moreover, it is able to obtain near-optimal results when aligning networks with themselves. Furthermore, in larger instances, our algorithm was still able to compete with the best performing method in this regard.Christian Blum and Guillem Rodríguez Corominas, Spain were supported by grants PID2019-104156GB-I00 and TED2021- 129319B-I00 funded by MCIN/AEI/10.13039/501100011033. Maria J. Blesa acknowledges support from AEI, Spain under grant PID2020-112581GB-C21 (MOTION) and the Catalan Agency for Management of University and Research Grants (AGAUR), Spain under grant 2017-SGR-786 (ALBCOM).Peer ReviewedPostprint (published version

A biased random key genetic algorithm for the weighted independent domination problem

This work deals with an NP-hard problem in graphs known as the weighted independent domination problem. We propose a biased random key genetic algorithm for solving this problem. The most important part of the proposed algorithm is a decoder that translates any vector of real-values into valid solutions to the tackled problem. The experimental results, in comparison to a state-of-the-art population-based iterated greedy algorithm from the literature, show that our proposed approach has advantages over the state-of-the-art algorithm in the context of the more dense graphs in which edges have higher weights than vertices.Peer ReviewedPostprint (author's final draft

Finding edge-disjoint paths in networks by means of artificial ant colonies

One of the basic operations in communication networks consists in establishing routes for connection requests between physically separated network nodes. In many situations, either due to technical constraints or to quality-of-service and survivability requirements, it is required that no two routes interfere with each other. These requirements apply in particular to routing and admission control in large-scale, high-speed and optical networks. The same requirements also arise in a multitude of other applications such as real-time communications, VLSI design, scheduling, bin packing, and load balancing. This problem can be modeled as a combinatorial optimization problem as follows. Given a graph G representing a network topology, and a collection T={(s_1,t_1)...(s_k,t_k)} of pairs of vertices in G representing connection request, the maximum edge-disjoint paths problem is an NP-hard problem that consists in determining the maximum number of pairs in T that can be routed in G by mutually edge-disjoint s_i-t_i paths. We propose an ant colony optimization (ACO) algorithm to solve this problem. ACO algorithms are approximate algorithms that are inspired by the foraging behavior of real ants. The decentralized nature of these algorithms makes them suitable for the application to problems arising in large-scale environments. First, we propose a basic version of our algorithm in order to outline its main features. In a subsequent step we propose several extensions of the basic algorithm and we conduct an extensive parameter tuning in order to show the usefulness of those extensions. In comparison to a multi-start greedy approach, our algorithm generates in general solutions of higher quality in a shorter amount of time. In particular the run-time behaviour of our algorithm is one of its important advantages.Postprint (published version

A review on DISC 2005, the 19th International Symposium on Distributed Computing

DISC is an international symposium on the theory, design, analysis, implementation and application of distributed systems and networks. The well-known International Symposium on Distributed Computing is organized annually in cooperation with the European Association for Theoretical Computer Science (EATCS). This is a review on the 19th International Symposium on Distributed Computing, which took place in Kraków, Poland, on September 26--29, 2005. The proceedings of DISC 2005 are published by Springer, as volume 3724 of the Lecture Notes in Computer Science (LNCS) series. The conference website can be found at www.mimuw.edu.pl/~disc2005.Postprint (published version