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See detailA GP Hyper-Heuristic Approach for Generating TSP Heuristics
Duflo, Gabriel UL; Kieffer, Emmanuel UL; Brust, Matthias R. UL et al

in 33rd IEEE International Parallel & Distributed Processing Symposium (IPDPS 2019) (2019, May 20)

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See detailTackling Large-Scale and Combinatorial Bi-level Problems with a Genetic Programming Hyper-heuristic
Kieffer, Emmanuel UL; Danoy, Grégoire UL; Bouvry, Pascal UL et al

in IEEE Transactions on Evolutionary Computation (2019)

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is a NP-hard problem. In this work, we tackle large-scale and combinatorial bi-level problems using GP ... [more ▼]

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is a NP-hard problem. In this work, we tackle large-scale and combinatorial bi-level problems using GP Hyper-heuristics, i.e., an approach that permits to train heuristics like a machine learning model. Our contribution aims at targeting the intensive and complex lower-level optimizations that occur when solving a large-scale and combinatorial bi-level problem. For this purpose, we consider hyper-heuristics through heuristic generation. Using a GP hyper-heuristic approach, we train greedy heuristics in order to make them more reliable when encountering unseen lower-level instances that could be generated during bi-level optimization. To validate our approach referred to as GA+AGH, we tackle instances from the Bi-level Cloud Pricing Optimization Problem (BCPOP) that model the trading interactions between a cloud service provider and cloud service customers. Numerical results demonstrate the abilities of the trained heuristics to cope with the inherent nested structure that makes bi-level optimization problems so hard. Furthermore, it has been shown that training heuristics for lower-level optimization permits to outperform human-based heuristics and metaheuristics which constitute an excellent outcome for bi-level optimization. [less ▲]

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See detailGP hyper-heuristic for the travelling salesman problem
Duflo, Gabriel UL; Kieffer, Emmanuel UL; Danoy, Grégoire UL et al

Scientific Conference (2019, January 29)

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See detailA Memory-Based Label Propagation Algorithm for Community Detection
Fiscarelli, Antonio Maria UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Complex Networks and Their Applications VII (2019)

The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dissimilarity between them. Several methods have been proposed but many of ... [more ▼]

The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dissimilarity between them. Several methods have been proposed but many of them are not suitable for large-scale networks because they have high complexity and use global knowledge. The Label Propagation Algorithm (LPA) assigns a unique label to every node and propagates the labels locally, while applying the majority rule to reach a consensus. Nodes which share the same label are then grouped into communities. Although LPA excels with near linear execution time, it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a novel LPA where each node implements memory and the decision rule takes past states of the network into account. We demonstrate through extensive experiments on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world networks that MemLPA outperforms most of state-of-the-art community detection algorithms. [less ▲]

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See detailA Memory-Based Label Propagation Algorithm for Community Detection
Fiscarelli, Antonio Maria UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Aiello, Luca Maria; Cherifi, Chantal; Cherifi, Hocine (Eds.) et al Complex Networks and Their Applications VII (2018, December 02)

The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dis- similarity between them. Several methods have been proposed but many of ... [more ▼]

The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dis- similarity between them. Several methods have been proposed but many of them are not suitable for large-scale networks because they have high complexity and use global knowledge. The Label Propagation Algorithm (LPA) assigns a unique label to every node and propagates the labels locally, while applying the majority rule to reach a consensus. Nodes which share the same label are then grouped into communities. Although LPA excels with near linear execution time, it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a novel LPA where each node imple- ments memory and the decision rule takes past states of the network into account. We demonstrate through extensive experiments on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world net- works that MemLPA outperforms most of state-of-the-art community detection algorithms. [less ▲]

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See detailA Competitive Approach for Bi-Level Co-Evolution
Kieffer, Emmanuel UL; Danoy, Grégoire UL; Bouvry, Pascal UL et al

in 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (2018, May 25)

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See detailOn Standardised UAV Localisation and Tracking Systems in Smart Cities
Samir Labib, Nader UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

Poster (2018, May)

In the near future, more than two thirds of the world’s population is expected to be living in cities and hence, with the aim of being proactive and finding innovative and sustainable solutions ... [more ▼]

In the near future, more than two thirds of the world’s population is expected to be living in cities and hence, with the aim of being proactive and finding innovative and sustainable solutions, governments have made smart cities one of their priority areas of research. Smart cities are sustainable, inclusive and prosperous greener cities that foster enabling smart Information and Communication Technologies (smart ICT) like Internet-of-Things (IoT), cloud computing and big data to facilitate services such as mobility, governance, utility and energy management. As these services depend heavily on data collected by sensors, Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising IoT devices for smart cities thanks to their mobility, agility and customizability of onboard sensors. UAVs found use in a wide array of applications expanding beyond military to more commercial ones, ranging from monitoring, surveillance, mapping to parcel delivery and more demanding applications that require UAVs to operate in heterogeneous swarms in a shared low-altitude airspace over populated cities. However, as the number of UAVs continues to grow and as their sensing, actuation, communication and control capabilities become increasingly sophisticated, UAV deployment in smart cities is faced with a set of fundamental challenges in their safe operation and management. These challenges emphasize the need for establishing globally-harmonised regulations and internationally-agreed-upon technical standards to govern the rapid technological advancements, as well as ensure a fair economy by encouraging market competition and lowering barriers to entry for newcomers. As various Standardisation Development Organisations (SDOs) recently recognised the need, importance and potential of such regulations, most have established dedicated working groups addressing UAVs. However, most current SDO committees focus on aspects such as vehicle categorisation, specifications and operational procedures, but one usually overlooked elementary topic is UAV localisation. Due to its importance and close relation to other technical subsystems, the lack of a resilient, scalable and efficient standardised UAV localisation and tracking system is one of the main obstructing barriers hindering the integration and interoperability of UAV swarms in smart cities and hence impeding the realisation of their vast application benefits. In this work, we focus on studying the fundamental technical requirements, specifications and functions of such UAV localisation and tracking system, and explore its relationship to and importance in 1) optimising path planning, flight scheduling and utilising shared airspace, 2) collision avoidance and conflict resolution in highly populated residential areas and 3) addressing privacy and data protection concerns that could arise from UAV monitoring and surveillance applications. Furthermore, for each of the three aspects, we analyse current SDOs efforts such as those put forth by EASA, EUCARE WG73 and ISO TC20/SC16 on UAV systems, ISO JTC1/SC41 on IoT and related technologies and ISO JTC1/SC27, EU Directive 95/46 EC and GDPR on security, privacy and data protection, in order to identify and prioritise future research questions in relation to UAV localisation, aiming to make a contribution towards narrowing the gap between research and existing technical standards by encouraging multimode standardisation. This research was conducted in collaboration with ILNAS - the Institut Luxembourgeois de la Normalisation, de l’Accréditation, de la Sécurité et qualité des produits et services (ILNAS) under the authority of the Minister of Economy, Luxembourg. [less ▲]

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See detailA Standardized Broker Model in Smart Cities
Liu, Chao UL; Varrette, Sébastien UL; Brust, Matthias R. UL et al

Poster (2018, May)

As urban residents are expected to represent more than 60 per cent of the world’s population by 2050, the current developments and interests in the “Smart City” concept are essential to enable the ... [more ▼]

As urban residents are expected to represent more than 60 per cent of the world’s population by 2050, the current developments and interests in the “Smart City” concept are essential to enable the successful transition to this new era. This paradigm relies on the integration of emerging Information and Communication Technologies (ICT), such as the Internet of Things (IoT), Cloud Computing, Big Data to manage assets and resources efficiently while facilitating the planning, construction, management and smart services within cities. While smart cities aim to enhance the quality, performance and interactivity of urban services at reduced cost, their realization is faced by many regulatory and technical challenges. Among these challenges, is the integration of renewable energy resources to the utility system of smart cities motivated by the increasingclimate change concerns. Adding further to its complexity, is the challenge of incorporating multiple renewable energy retailers in the same region each with their own pricing strategies due to the lack of a standardized metering indicator and billing system. These challenges create a need for an intelligent and standardized cloud-based energy broker to satisfying the end-user requests, and minimize expenses by efficiently selecting the most suitable energy retailer. In our work, a particular focus is raised towards the optimization of such energy brokering service which is motivated by the orchestration of a brokering role aiming to improve user experience and interaction with smart city services. Hence our main contribution is proposing a standardized intelligent broker model with smart trading strategies to cope with the dynamics and complexity of the energy retail market, while allocating energy resources based on endusers’ demands. This is achieved through the following steps: 1) studying a complete model of the broker service and involved parties within the exposed framework. 2) proposing a multiobjective heuristic to provide a dynamic optimization of the grid operations and resources, with full cyber-security, within the boundaries of the city. 3) analyzing the gaps among industry practices, market requirements and current technical standardization efforts at ISO/IEC JTC 1/ SC 38 (Cloud Computing and distributed platform) in order to pave the way to establishing standards in metering indicators and billing principles for cloud services this while keeping in mind privacy and data protection risks and regulations enforced by ISO JT1/SC 27 and EU General Data Protection Regulation effective May 2018. This research was conducted in collaboration with ILNAS - the Institut Luxem- bourgeois de la Normalisation, de l’Accréditation, de la Sécurité et qualité des produits et services (ILNAS) under the authority of the Minister of Economy, Luxembourg. [less ▲]

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See detailCollision Avoidance Effects on the Mobility of a UAV Swarm Using Chaotic Ant Colony with Model Predictive Control
Dentler, Jan Eric UL; Rosalie, Martin UL; Danoy, Grégoire UL et al

in Journal of Intelligent \& Robotic Systems (2018)

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a ... [more ▼]

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a novel mobility model has been presented in previous work, combining an Ant Colony algorithm with chaotic dynamics (CACOC). This work is extending CACOC by a Collision Avoidance (CA) mechanism and testing its efficiency in terms of area coverage by the UAV swarm. For this purpose, CACOC is used to compute UAV target waypoints which are tracked by model predictively controlled UAVs. The UAVs are represented by realistic motion models within the virtual robot experimentation platform (V-Rep). This environment is used to evaluate the performance of the proposed CACOC with CA algorithm in an area exploration scenario with 3 UAVs. Finally, its performance is analyzed using metrics. [less ▲]

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See detailBayesian optimization to enhance coverage performance of a swarm of UAV with chaotic dynamics
Kieffer, Emmanuel UL; Rosalie, Martin UL; Danoy, Grégoire UL et al

Scientific Conference (2018, February 26)

We introduce the optimization of CACOC through Bayesian Optimization. CACOC is based on a chaotic system, i.e. Rossler system whose behavior can be modified by tuning the α parameter. In order to evaluate ... [more ▼]

We introduce the optimization of CACOC through Bayesian Optimization. CACOC is based on a chaotic system, i.e. Rossler system whose behavior can be modified by tuning the α parameter. In order to evaluate the performance of CACOC for different value of α, the coverage metric has to be evaluated after simulation. The latter is time-consuming. Therefore, a surrogate-based optimization, i.e. Bayesian Optimization has been privilegied to tackle this issue. An analysis of the chaotic system with the obtained α value has been performed to compare the periodic orbits and their associated patterns. Numerical results show that the best α value avoid a waste of time in periodic region of the bifurcation diagram. Future works will focus on more complex chaotic system as well as new application domain of the optimized CACOC approach. [less ▲]

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See detailChaos-enhanced mobility models for multilevel swarms of UAVs
Rosalie, Martin UL; Danoy, Grégoire UL; Chaumette, Serge et al

in Swarm and Evolutionary Computation (2018)

The number of civilian and military applications using Unmanned Aerial Vehicles (UAVs) has increased during the last years and the forecasts for upcoming years are exponential. One of the current major ... [more ▼]

The number of civilian and military applications using Unmanned Aerial Vehicles (UAVs) has increased during the last years and the forecasts for upcoming years are exponential. One of the current major challenges consist in considering UAVs as autonomous swarms to address some limitations of single UAV usage such as autonomy, range of operation and resilience. In this article we propose novel mobility models for multi-level swarms of collaborating UAVs used for the coverage of a given area. These mobility models generate unpredictable trajectories using a chaotic solution of a dynamical system. We detail how the chaotic properties are used to structure the exploration of an unknown area and enhance the exploration part of an Ant Colony Optimization method. Empirical evidence of the improvement of the coverage efficiency obtained by our mobility models is provided via simulation. It clearly outperforms state-of-the-art approaches. [less ▲]

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See detailVisualizing the Template of a Chaotic Attractor
Olszewski, Maya Alexandra UL; Meder, Jeff Alphonse Antoine UL; Kieffer, Emmanuel UL et al

in 26th International Symposium on Graph Drawing and Network Visualization (GD 2018) (2018)

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See detailIntroduction to PDCO 2018
Danoy, Grégoire UL; Baz, Didier El; Boyer, Vincent et al

in 2018 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPS Workshops 2018, Vancouver, BC, Canada, May 21-25 2018 (2018)

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See detailRapidRMSD: Rapid determination of RMSDs corresponding to motions of flexible molecules
Neveu, Emilie; Popov, Petr; Hoffmann, Alexandre et al

in Bioinformatics (2018)

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See detailSingle and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters
Curi, María Eugenia; Carozzi, Lucía; Massobrio, Renzo et al

in Bioinspired Optimization Methods and Their Applications (2018)

This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the ... [more ▼]

This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the evolutionary search as simple as possible in order to scale up for solving large instances. The experimental evaluation is performed considering a set of real problem instances, including a real-life problem of analyzing biomedical information in the Parkinson's disease map project. The main results demonstrate that the proposed evolutionary approaches are able to compute accurate trade-off solutions and efficiently handle the problem instance involving biomedical information. [less ▲]

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See detailChaotic Traversal (CHAT): Very Large Graphs Traversal Using Chaotic Dynamics
Changaival, Boonyarit UL; Rosalie, Martin UL; Danoy, Grégoire UL et al

in International Journal of Bifurcation and Chaos (2017), 27(14), 1750215

Graph Traversal algorithms can find their applications in various fields such as routing problems, natural language processing or even database querying. The exploration can be considered as a first ... [more ▼]

Graph Traversal algorithms can find their applications in various fields such as routing problems, natural language processing or even database querying. The exploration can be considered as a first stepping stone into knowledge extraction from the graph which is now a popular topic. Classical solutions such as Breadth First Search (BFS) and Depth First Search (DFS) require huge amounts of memory for exploring very large graphs. In this research, we present a novel memoryless graph traversal algorithm, Chaotic Traversal (CHAT) which integrates chaotic dynamics to traverse large unknown graphs via the Lozi map and the Rössler system. To compare various dynamics effects on our algorithm, we present an original way to perform the exploration of a parameter space using a bifurcation diagram with respect to the topological structure of attractors. The resulting algorithm is an efficient and nonresource demanding algorithm, and is therefore very suitable for partial traversal of very large and/or unknown environment graphs. CHAT performance using Lozi map is proven superior than the, commonly known, Random Walk, in terms of number of nodes visited (coverage percentage) and computation time where the environment is unknown and memory usage is restricted. [less ▲]

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See detailCoverage optimization with connectivity preservation for UAV swarms applying chaotic dynamics
Rosalie, Martin UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in IEEE International Conference on Autonomic Computing (ICAC), Columbus 17-21 July 2017 (2017, August 11)

Cooperative usage of multiple UAVs as a swarm can deliver high-quality surveillance performance. However, the communication capabilities within the UAV swarm must be maintained for local data propagation ... [more ▼]

Cooperative usage of multiple UAVs as a swarm can deliver high-quality surveillance performance. However, the communication capabilities within the UAV swarm must be maintained for local data propagation to swarm members in favor of achieving an efficient global behavior. In this paper, we address the problem of optimizing two adversary criteria for such a UAV swarm: (a) maximizing the area coverage, while (b) preserving network connectivity. Our approach, called CACOC², solves the problem with a novel chaotic ant colony optimization approach, which combines an Ant Colony Optimization approach (ACO) with a chaotic dynamical system. CACOC² employs swarming behavior to obtain UAV clustering that result in maximized area coverage and preserved network connectivity. We show by extensive simulations how the size of the UAV swarm influences the coverage and connectivity. A metrics comparison chart shows the correlation of coverage and connectivity metrics. [less ▲]

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See detailArea exploration with a swarm of UAVs combining deterministic Chaotic Ant Colony Mobility with position MPC
Rosalie, Martin UL; Dentler, Jan Eric UL; Danoy, Grégoire UL et al

in 2017 International Conference on Unmanned Aircraft Systems (ICUAS) (2017, July 27)

The recent advances in Unmanned Aerial Vehicles (UAVs) technology permit to develop new usages for them. One of the current challenges is to operate UAVs as an autonomous swarm. In this domain we already ... [more ▼]

The recent advances in Unmanned Aerial Vehicles (UAVs) technology permit to develop new usages for them. One of the current challenges is to operate UAVs as an autonomous swarm. In this domain we already proposed a new mobility model using Ant Colony Algorithms combined with chaotic dynamics (CACOC) to enhance the coverage of an area by a swarm of UAVs. In this paper we propose to consider this mobility model as waypoints for real UAVs. A control model of the UAVs is deployed to test the efficiency of the coverage of an area by the swarm. We have tested our approach in a realistic robotics simulator (V-Rep) which is connected with ROS. We compare the performance in terms of coverage using several metrics to ensure that this mobility model is efficient for real UAVs. [less ▲]

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See detailASIMUT project: Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques
Bouvry, Pascal UL; Chaumette, Serge; Danoy, Grégoire UL et al

in DroNet'17 Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications (2017, June 23)

This document summarizes the activities and results of the ASIMUT project (Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques) carried out by the consortium ... [more ▼]

This document summarizes the activities and results of the ASIMUT project (Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques) carried out by the consortium composed of Thales, Fraunhofer IOSB, Fly-n-Sense, University of Bordeaux and University of Luxembourg. Funded by the European Defence Agency (EDA), the objectives of the ASIMUT project are to design, implement and validate algorithms that will allow the efficient usage of autonomous swarms of Unmanned Aerial Vehicles (UAVs) for surveillance missions. [less ▲]

Detailed reference viewed: 67 (12 UL)