Algorithms for the weighted independent domination problem

El problema de la dominació independent ponderada és un problema NP-hard d'optimització combinatòria en grafs. Aquest problema només ha estat abordat a la literatura per enfocaments de programació lineal entera, heurístiques voraces i diferents versions d'algoritmes voraços iteratius basats en poblacions. En aquest projecte, primer apliquem una millora sobre les heurístiques voraces existents. Això ho fem implementant les versions rollout d'aquestes heurístiques i provant-les en un marc multistart on són aplicades de forma probabilística. En segon lloc, implementem tres versions d'un algorisme genètic esbiaixat de clau aleatòria. La diferència entre aquestes versions es troba en la forma en què els individus són descodificats en solucions viables del problema. Els resultats mostren que els algorismes desenvolupats poden competir amb els que són estat de l'art en el conjunt d'instàncies relativament petites. No obstant això, amb una mida creixent de les instàncies del problema, els nostres algorismes no poden arribar al nivell dels resultats obtinguts per l'algorisme més punter. Tot i això, els nostres algorismes poden ser millorats de moltes formes diferents, les quals expliquem en detall. Per tant, creiem que els nostres algorismes haurien de ser més estudiats en treballs futurs.The weighted independent domination problem is an NP-hard combinatorial optimization problem in graphs. This problem has only been tackled in the literature by integer linear programming approaches, by Greedy heuristics, and by different versions of a population-based iterated greedy algorithm. In this project, we first improve over the existing Greedy heuristics. This is done by implementing the rollout versions of these heuristics, and by testing them in a multistart framework in which they are applied in a probabilistic way. Second, we implement three versions of a biased random key genetic algorithm. The difference between these versions is found in the way in which individuals are decoded into feasible solutions to the problem. Moreover, we study the rollout versions of the corresponding decoders. Our results show that the developed algorithms can compete with the state of the art in the group of rather small-scale problem instances. However, with a growing size of the problem instances, our algorithms can not quite match the results of the current state-of-the-art algorithm. Nevertheless, our algorithms can potentially be improved in several different ways, which we explain in detail. Therefore, we believe that our algorithms should be further studied in future work

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

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

AntNetAlign: A software package for network alignment

In this paper we introduce AntNetAlign, an open-source tool (written in C++) that implements an Ant Colony Optimization (ACO) for solving the Network Alignment (NA) problem, a well-known hard optimization problem with important applications in different areas, such as biology or social networks. Specifically, given two input networks, the tool finds an alignment between them (i.e., a mapping between the respective nodes) that optimizes one out of the three main topological measures. Additionally, it can make use of user-defined pairwise similarities between nodes during its construction phase, allowing for the use of more application-dependent information in order to increase its performance. Results show that AntNetAlign outperforms other state-of-the-art algorithms in two out of the three aforementioned topological scores within a reasonable amount of time, and is able to achieve competitive results in the context of larger instances (Rodríguez Corominas et al., 2023). Furthermore, a new version of the algorithm, which makes use of Negative Learning, was able to further improve these results, specially in the EC score (Corominas et al., 2022).Guillem Rodríguez Corominas gets support from the Department of Research and Universities of the Government of Catalonia by means of an ESF-founded pre-doctoral grant of the Catalan Agency for Management of University and Research Grants (AGAUR, Catalonia, Spain), under ref. number 2022 FI_B 00903. Christian Blum was supported by two grants funded by MCIN/AEI/10.13039/501100011033: AEI, Spain PID2019-104156GB-I00, and TED2021-129319B-I00. Maria J. Blesa acknowledges support from AEI, Spain under grant PID-2020-112581GB-C21 (MOTION) and the Catalan Agency for Management of University and Research Grants (AGAUR , Catalonia, Spain), under grant 2021-SGR-01419 (ALBCOM).Peer ReviewedPostprint (published version

Famílies botàniques de plantes medicinals

Facultat de Farmàcia, Universitat de Barcelona. Ensenyament: Grau de Farmàcia, Assignatura: Botànica Farmacèutica, Curs: 2013-2014, Coordinadors: Joan Simon, Cèsar Blanché i Maria Bosch.Els materials que aquí es presenten són els recull de 175 treballs d’una família botànica d’interès medicinal realitzats de manera individual. Els treballs han estat realitzat per la totalitat dels estudiants dels grups M-2 i M-3 de l’assignatura Botànica Farmacèutica durant els mesos d’abril i maig del curs 2013-14. Tots els treballs s’han dut a terme a través de la plataforma de GoogleDocs i han estat tutoritzats pel professor de l’assignatura i revisats i finalment co-avaluats entre els propis estudiants. L’objectiu principal de l’activitat ha estat fomentar l’aprenentatge autònom i col·laboratiu en Botànica farmacèutica

Metaheuristics for network alignment: an integrative view

The Network Alignment problem is an NP-complete Combinatorial Optimization problem in graphs. The goal is to find an alignment between the input networks, i.e., a mapping between their respective nodes, such that the topological and functional structure is well preserved. During the last decades, many methods have been proposed for solving the problem. However, many of them are designed only for specific areas and applications. In this thesis, we propose AntNetAlign, a new Ant Colony Optimization Algorithm for solving the Network Alignment problem with an integrative view. The key novelties of this approach are the following. First, it can incorporate any pairwise node similarity information to guide the construction process. This similarity is not restricted to any specific kind, allowing for high versatility while applying our method in different contexts. Second, it combines this similarity metric with an improvement measure that depends on the current state of the construction, thus providing both a global and local view of the undergoing construction process. Third, it is able to optimize any of the three considered topological quality measures. And fourth, it is complemented with three different selection strategies. The experimental results obtained over a real-world set of Protein-Protein Interaction networks show that out algorithm is able to outperform other state-of-the-art algorithms from the literature in two out of three of the tested scores. More specifically, our method obtains significantly better results in the superior S3 score in a reasonable amount of time. Moreover, AntNetAlign obtains nearly-optimal solutions when aligning networks with themselves. Additional experimental results show that the good performance of our algorithm may be justified by its high resistance to noise

Metaheuristics for Network Alignment: An Integrative View

The Network Alignment problem is an NP-complete Combinatorial Optimization problem in graphs. The goal is to find an alignment between the input networks, i.e., a mapping between their respective nodes, such that the topological and functional structure is well preserved. During the last decades, many methods have been proposed for solving the problem. However, many of them are designed only for specific areas and applications. In this thesis, we propose AntNetAlign, a new Ant Colony Optimization Algorithm for solving the Network Alignment problem with an integrative view. The key novelties of this approach are the following. First, it can incorporate any pairwise node similarity information to guide the construction process. This similarity is not restricted to any specific kind, allowing for high versatility while applying our method in different contexts. Second, it combines this similarity metric with an improvement measure that depends on the current state of the construction, thus providing both a global and local view of the undergoing construction process. Third, it is able to optimize any of the three considered topological quality measures. And fourth, it is complemented with three different selection strategies. The experimental results obtained over a real-world set of Protein-Protein Interaction networks show that out algorithm is able to outperform other state-of-the-art algorithms from the literature in two out of three of the tested scores. More specifically, our method obtains significantly better results in the superior S3 score in a reasonable amount of time. Moreover, AntNetAlign obtains nearly-optimal solutions when aligning networks with themselves. Additional experimental results show that the good performance of our algorithm may be justified by its high resistance to noise

Algorithms for the weighted independent domination problem

El problema de la dominació independent ponderada és un problema NP-hard d'optimització combinatòria en grafs. Aquest problema només ha estat abordat a la literatura per enfocaments de programació lineal entera, heurístiques voraces i diferents versions d'algoritmes voraços iteratius basats en poblacions. En aquest projecte, primer apliquem una millora sobre les heurístiques voraces existents. Això ho fem implementant les versions rollout d'aquestes heurístiques i provant-les en un marc multistart on són aplicades de forma probabilística. En segon lloc, implementem tres versions d'un algorisme genètic esbiaixat de clau aleatòria. La diferència entre aquestes versions es troba en la forma en què els individus són descodificats en solucions viables del problema. Els resultats mostren que els algorismes desenvolupats poden competir amb els que són estat de l'art en el conjunt d'instàncies relativament petites. No obstant això, amb una mida creixent de les instàncies del problema, els nostres algorismes no poden arribar al nivell dels resultats obtinguts per l'algorisme més punter. Tot i això, els nostres algorismes poden ser millorats de moltes formes diferents, les quals expliquem en detall. Per tant, creiem que els nostres algorismes haurien de ser més estudiats en treballs futurs.The weighted independent domination problem is an NP-hard combinatorial optimization problem in graphs. This problem has only been tackled in the literature by integer linear programming approaches, by Greedy heuristics, and by different versions of a population-based iterated greedy algorithm. In this project, we first improve over the existing Greedy heuristics. This is done by implementing the rollout versions of these heuristics, and by testing them in a multistart framework in which they are applied in a probabilistic way. Second, we implement three versions of a biased random key genetic algorithm. The difference between these versions is found in the way in which individuals are decoded into feasible solutions to the problem. Moreover, we study the rollout versions of the corresponding decoders. Our results show that the developed algorithms can compete with the state of the art in the group of rather small-scale problem instances. However, with a growing size of the problem instances, our algorithms can not quite match the results of the current state-of-the-art algorithm. Nevertheless, our algorithms can potentially be improved in several different ways, which we explain in detail. Therefore, we believe that our algorithms should be further studied in future work

Algorithms for the weighted independent domination problem

El problema de la dominació independent ponderada és un problema NP-hard d'optimització combinatòria en grafs. Aquest problema només ha estat abordat a la literatura per enfocaments de programació lineal entera, heurístiques voraces i diferents versions d'algoritmes voraços iteratius basats en poblacions. En aquest projecte, primer apliquem una millora sobre les heurístiques voraces existents. Això ho fem implementant les versions rollout d'aquestes heurístiques i provant-les en un marc multistart on són aplicades de forma probabilística. En segon lloc, implementem tres versions d'un algorisme genètic esbiaixat de clau aleatòria. La diferència entre aquestes versions es troba en la forma en què els individus són descodificats en solucions viables del problema. Els resultats mostren que els algorismes desenvolupats poden competir amb els que són estat de l'art en el conjunt d'instàncies relativament petites. No obstant això, amb una mida creixent de les instàncies del problema, els nostres algorismes no poden arribar al nivell dels resultats obtinguts per l'algorisme més punter. Tot i això, els nostres algorismes poden ser millorats de moltes formes diferents, les quals expliquem en detall. Per tant, creiem que els nostres algorismes haurien de ser més estudiats en treballs futurs.The weighted independent domination problem is an NP-hard combinatorial optimization problem in graphs. This problem has only been tackled in the literature by integer linear programming approaches, by Greedy heuristics, and by different versions of a population-based iterated greedy algorithm. In this project, we first improve over the existing Greedy heuristics. This is done by implementing the rollout versions of these heuristics, and by testing them in a multistart framework in which they are applied in a probabilistic way. Second, we implement three versions of a biased random key genetic algorithm. The difference between these versions is found in the way in which individuals are decoded into feasible solutions to the problem. Moreover, we study the rollout versions of the corresponding decoders. Our results show that the developed algorithms can compete with the state of the art in the group of rather small-scale problem instances. However, with a growing size of the problem instances, our algorithms can not quite match the results of the current state-of-the-art algorithm. Nevertheless, our algorithms can potentially be improved in several different ways, which we explain in detail. Therefore, we believe that our algorithms should be further studied in future work

Negative learning Ant colony optimization for network alignment

The Network Alignment problem is a hard Combinatorial Optimization problem with a wide range of applications, especially in computational biology. Given two (or more) networks, the goal is to find a mapping between their respective nodes that preserves the topological and functional structure of the networks. In this work we extend a novel ant colony optimization approach for network alignment by adding a recently proposed Negative Learning mechanism. In particular, information for Negative Learning is obtained by solving sub-instances of the tackled problem instances at each iteration by means of an Integer Linear Programming solver. The results show that the proposed algorithm not only outperforms the standard ant colony optimization approach but also current state-of-the-art methods from the literature.Peer reviewe