WorldCat Identities

Idoumghar, Lhassane

Overview
Works: 30 works in 66 publications in 3 languages and 994 library holdings
Genres: Conference papers and proceedings 
Roles: Editor, Opponent, htt, Other, Author, Thesis advisor
Publication Timeline
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Most widely held works by Lhassane Idoumghar
Swarm Intelligence Based Optimization Second International Conference, ICSIBO 2016, Mulhouse, France, June 13-14, 2016, Revised Selected Papers by Patrick Siarry( )

27 editions published between 2014 and 2016 in 3 languages and held by 717 WorldCat member libraries worldwide

This book constitutes the thoroughly refereed post-conference proceedings of the 1st International Conference on Swarm Intelligence Based Optimization, ICSIBO 2014, held in Mulhouse, France, in May 2014. The 20 full papers presented were carefully reviewed and selected from 48 submissions. Topics of interest presented and discussed in the conference focuses on the theoretical progress of swarm intelligence metaheuristics and their applications in areas such as: theoretical advances of swarm intelligence metaheuristics, combinatorial, discrete, binary, constrained, multi-objective, multi-modal, dynamic, noisy, and large-scale optimization, artificial immune systems, particle swarms, ant colony, bacterial foraging, artificial bees, fireflies algorithm, hybridization of algorithms, parallel/distributed computing, machine learning, data mining, data clustering, decision making and multi-agent systems based on swarm intelligence principles, adaptation and applications of swarm intelligence principles to real world problems in various domains
Artificial evolution : 14th International Conference, Évolution Artificielle, EA 2019, Mulhouse, France, October 29-30, 2019, revised selected papers by EA (Conference)( )

7 editions published in 2020 in English and held by 223 WorldCat member libraries worldwide

This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Artificial Evolution, EA 2019, held in Mulhouse, France, in October 2019. The 16 revised papers were carefully reviewed and selected from 33 submissions. The papers cover a wide range of topics in the field of artificial evolution, such as evolutionary computation, evolutionary optimization, co-evolution, artificial life, population dynamics, theory, algorithmic and modeling, implementations, application of evolutionary paradigms to the real world (industry, biosciences ...), other biologically-inspired paradigms (swarm, artificial ants, artificial immune systems, cultural algorithms ...), memetic algorithms, multi-objective optimization, constraint handling, parallel algorithms, dynamic optimization, machine learning and hybridization with other soft computing techniques
Artificial Evolution : 14th International Conference, Évolution Artificielle, EA 2019, Mulhouse, France, October 29-30, 2019, Revised Selected Papers by Lhassane Idoumghar( )

2 editions published in 2020 in English and held by 22 WorldCat member libraries worldwide

This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Artificial Evolution, EA 2019, held in Mulhouse, France, in October 2019. The 16 revised papers were carefully reviewed and selected from 33 submissions. The papers cover a wide range of topics in the field of artificial evolution, such as evolutionary computation, evolutionary optimization, co-evolution, artificial life, population dynamics, theory, algorithmic and modeling, implementations, application of evolutionary paradigms to the real world (industry, biosciences...), other biologically-inspired paradigms (swarm, artificial ants, artificial immune systems, cultural algorithms...), memetic algorithms, multi-objective optimization, constraint handling, parallel algorithms, dynamic optimization, machine learning and hybridization with other soft computing techniques
Itérations chaotiques pour la sécurité de l'information dissimulée by Nicolas Friot( Book )

2 editions published in 2014 in French and held by 2 WorldCat member libraries worldwide

Discrete dynamical systems by chaotic or asynchronous iterations have proved to be highly interesting toolsin the field of computer security, thanks to their unpredictible behavior obtained under some conditions. Moreprecisely, these chaotic iterations possess the property of topological chaos and can be programmed in anefficient way. In the state of the art, they have turned out to be really interesting to use notably through digitalwatermarking schemes. However, despite their multiple advantages, these existing algorithms have revealedsome limitations. So, these PhD thesis aims at removing these constraints, proposing new processes whichcan be applied both in the field of digital watermarking and of steganography. We have studied these newschemes on two aspects: the topological security and the security based on a probabilistic approach. Theanalysis of their respective security level has allowed to achieve a comparison with the other existing processessuch as, for example, the spread spectrum. Application tests have also been conducted to steganalyse and toevaluate the robustness of the algorithms studied in this PhD thesis. Thanks to the obtained results, it has beenpossible to determine the best adequation of each processes with targeted application fields as, for example,the anonymity on the Internet, the contribution to the development of the semantic web, or their use for theprotection of digital documents. In parallel to these scientific research works, several valorization perspectiveshave been proposed, aiming at creating a company of innovative technology
Combining approaches for predicting genomic evolution by Bassam Alkindy( Book )

2 editions published in 2015 in English and held by 2 WorldCat member libraries worldwide

In Bioinformatics, understanding how DNA molecules have evolved over time remains an open and complex problem. Algorithms have been proposed to solve this problem, but they are limited either to the evolution of a given character (forexample, a specific nucleotide), or conversely focus on large nuclear genomes (several billion base pairs), the latter havingknown multiple recombination events - the problem is NP complete when you consider the set of all possible operationson these sequences, no solution exists at present. In this thesis, we tackle the problem of reconstruction of ancestral DNAsequences by focusing on the nucleotide chains of intermediate size, and have experienced relatively little recombinationover time: chloroplast genomes. We show that at this level the problem of the reconstruction of ancestors can be resolved, even when you consider the set of all complete chloroplast genomes currently available. We focus specifically on the orderand ancestral gene content, as well as the technical problems this raises reconstruction in the case of chloroplasts. Weshow how to obtain a prediction of the coding sequences of a quality such as to allow said reconstruction and how toobtain a phylogenetic tree in agreement with the largest number of genes, on which we can then support our back in time- the latter being finalized. These methods, combining the use of tools already available (the quality of which has beenassessed) in high performance computing, artificial intelligence and bio-statistics were applied to a collection of more than450 chloroplast genomes
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks by Hassan Ismail Fawaz( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

Méthodes algorithmiques pour l'allocation de fréquences by Lhassane Idoumghar( Book )

2 editions published in 2002 in French and held by 2 WorldCat member libraries worldwide

La génération d'un plan de fréquences est une tâche difficile dans le processus de planification, qui peut conduire à des plans de fréquences peu adéquats d'un point de vue métier. En effet, le processus de génération s'appuie d'une part sur une modélisation des contraintes existant entre les points de service du réseau étudié, et d'autre part sur une optimisation combinatoire qui vise à satisfaire ces contraintes. Cette optimisation combinatoire fournit une solution optimale d'un point de vue mathématique, mais selon la finesse de modélisation des contraintes, la solution générée peut être inutilisable dans la réalité. Dans cette thèse, nous présentons de nouvelles méthodes algorithmiques permettant de résoudre efficacement le problème d'allocation de fréquences dans le cadre de la radiodiffusion. L'utilisation de ces nouvelles approches a montré que la qualité des solutions obtenues est nettement meilleure que les meilleures solutions opérationnelles existant dans ce domaine
Deep learning for time series classification: a review by Hassan Ismail Fawaz( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

Sizing and Operation of Multi-Energy Hydrogen-Based Microgrids by Bei Li( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

With the development of distributed, renewable energy sources, microgrids can be expected to play an important role in future power systems, not only to reduce emissions and maximize local energy use, but also to improve system resilience. Due to the intermittence and uncertainty of renewable sources (such as photovoltaics or wind turbines), energy storage systems should also be integrated. However, determining their size and how to operate them remains challenging, especially as the adopted control strategy impacts sizing results, and for systems considering multiple, interdependent forms of energy. This thesis therefore contributes to solving the sizing and operation problems of full-electric and multi-energy (electricity, gas, heat, cooling and/or hydrogen) microgrids integrating storage systems.First, based on the characteristics of different components, a mathematical model of a microgrid is built. Then, the operation problem is formulated as a mixed integer linear problem (MILP), based on an objective function (minimize the operation cost) and different constraints (maximum power, startup/shutdown times, state-of-charge limits, etc.). Next, a co-optimization structure is presented to solve the sizing problem using a genetic algorithm. This specific structure enables to search for sizing values based on the operation results, which enables determining the best sizing for the selected operation strategy.Using the above method, four specific problems are then studied. The first one focuses on sizing a full-electric islanded microgrid combining battery and hydrogen storage systems for short and long-term storage, respectively. Results for two types of operation strategies are compared: the MILP approach and a rule-based strategy. A one-hour one-year rolling horizon simulation is used to check the validity of the sizing results.Second, a multi-energy islanded microgrid with different types of loads is studied. Specifically, the influence of three factors on sizing results is analyzed: the operation strategy, the accuracy of load and renewable generation forecasts, and the degradation of energy storage systems.Third, the work focuses on a grid-connected microgrid attached to a gas, electricity and heat hybrid network. Specifically, the resilience of the network is considered in order to maximize resistance to contingency events. Betweenness centrality is used to find the worst case under contingency events and analyze their impact on sizing results. Two test systems of different sizes are used with the proposed method and a study of its sensitivity to various parameters is carried out.Fourth, a structure with multiple grid-connected multi-energy-supply microgrids is considered, and an algorithm for determining electricity prices is developed. This price is used for energy exchanges between microgrids and load service entities interacting with the utility. The proposed co-optimization method is deployed to search for the best price that maximizes benefits to all players. Simulations on a large system show that the obtained price returns better results than a basic time-of-use price and helps reduce the operation cost of the whole system. To reduce the computation time, a neural network is presented to estimate the operation of the whole system and enable obtaining results faster with a limited impact on performance. At last, a sizing algorithm for grid-connected multi-energy supply microgrids operating under different prices is presented.The obtained results on these different applications show the usefulness of the proposed method, which is a promising contribution toward the creation of advanced design tools for such microgrids
Méthodes algorithmiques pour l'optimisation mono-objectif et multi-objectif : application aux réseaux de radiodiffusion by Akram Bedoui( )

1 edition published in 2015 in French and held by 1 WorldCat member library worldwide

The purpose of my thesis is the dynamic construction of radio communication networks subject to multiple constraints and the optimal (if possible) use of the set of available frequencies at time t. This is an NP-Hard problem with important economical issues. I have designed and used original hybrid meta-heuristics for solving this kind of problems and providing the best possible QoS. Frequencies are rare and expansive therefore we can ask if a single frequency would not be enough, for a while, in order to cover the needs of a given geographical area. This would allow the use of the remaining frequencies for other applications. This is the principle of Single Frequency Networks (SFN) which necessitate the simultaneous optimization of transmission delays, of the allocated frequency and of their design. I have designed an original and e_cient software which performs these operations. We give experimental results for real benchmarks provided by TDF
Une approche de patrouille multi-agents pour la détection d'évènements by Elie Tagne-Fute( )

1 edition published in 2013 in French and held by 1 WorldCat member library worldwide

To fight effectively against scourges like forest fires , brush fires or natural disasters is a major issue in many cities worldwide.With the advent of technology represented by sensor networks , detection of these phenomena becomes easier .Indeed , sensors can be deployed in remote areas and they are enough to cover the entire environment to monitor, an alert can be given directly by the sensor has detected a certain type of event (fire, earthquake ... ) .The control center has received the alert may then decide to intervene in the area in question .Our work takes place in the context of the detection of phenomena by a sensor network , assuming that the environment is known and that the sensors are mobile, wireless and insufficient to cover the entire environment to be monitored.Speaking of monitoring a small number of mobile entities requires regularly browse some critical environmental areas, which can be likened to a patrol task .In this thesis , we focused on identifying strategies patrol multi-sensor applied to the detection of events.A similar problem to ours is the multi-agent patrolling in a known environment .This problem is to regularly visit the nodes of a graph (representing the environment) by agents.The sensors can be considered as agents with limited resources , in terms of energy in particular.The framework of multi- agents and techniques proposed to solve patrol can not be used here .After mathematically formulated the problem of multi-sensor patrol applied to the detection of events, we propose an approximate solution technique based on ant colonies .Simulations were made ​​considering different scenarios ( environmental topologies populations sensors appearances events ) to assess the relevance of our approach.The experimental results show that our approach identifies strategies patrol satisfactory in the majority of scenarios
Algorithmes et architectures multi-agents pour la gestion de l'énergie dans les réseaux électriques intelligents by Robin Roche( )

1 edition published in 2012 in English and held by 1 WorldCat member library worldwide

Due to the convergence of several profound trends in the energy sector, smart gridsare emerging as the main paradigm for the modernization of the electric grid. Smartgrids hold many promises, including the ability to integrate large shares of distributedand intermittent renewable energy sources, energy storage and electric vehicles, as wellas the promise to give consumers more control on their energy consumption. Such goalsare expected to be achieved through the use of multiple technologies, and especially ofinformation and communication technologies, supported by intelligent algorithms.These changes are transforming power grids into even more complex systems, thatrequire suitable tools to model, simulate and control their behaviors. In this dissertation,properties of multi-agent systems are used to enable a new systemic approach to energymanagement, and allow for agent-based architectures and algorithms to be defined. Thisnew approach helps tackle the complexity of a cyber-physical system such as the smart gridby enabling the simultaneous consideration of multiple aspects such as power systems, thecommunication infrastructure, energy markets, and consumer behaviors. The approach istested in two applications: a “smart” energy management system for a gas turbine powerplant, and a residential demand response system.An energy management system for gas turbine power plants is designed with the objectiveto minimize operational costs and emissions, in the smart power generation paradigm.A gas turbine model based on actual data is proposed, and used to run simulations witha simulator specifically developed for this problem. A metaheuristic achieves dynamicdispatch among gas turbines according to their individual characteristics. Results showthat the system is capable of operating the system properly while reducing costs and emissions.The computing and communication requirements of the system, resulting from theselected architecture, are also evaluated.With other demand-side management techniques, demand response enables reducingload during a given duration, for example in case of a congestion on the transmissionsystem. A demand response system is proposed and relies on the use of the assets ofresidential customers to curtail and shift local loads (hybrid electric vehicles, air conditioning,and water heaters) so that the total system load remains under a given threshold.Aggregators act as interfaces between grid operators and a demand response market. Asimulator is also developed to evaluate the performance of the proposed system. Resultsshow that the system manages to maintain the total load under a threshold by usingavailable resources, without compromising the steady-state stability of the distributionsystem
Artificial evolution : 7th international conference, Evolution Artificielle, EA 2005, Lille, France, October 26-28, 2005 : revised selected papers by El-Ghazali Talbi( )

1 edition published in 2020 in English and held by 1 WorldCat member library worldwide

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Contributions on planning and optimization in modern healthcare system by Liyang Xiao( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

Operations research (OR) plays an important role in healthcare system. In recent years, rehabilitation hospitals have been emerging to meet the increasing needs for rehabilitation services due to the ageing population trend. However, the healthcare management in rehabilitation sectors is undeveloped and most of the rehabilitation hospitals (departments) are managed by experience. In this thesis, we deal with a treatment scheduling problem in rehabilitation hospitals. The objective is to facilitate the scheduling process. More importantly, our work aims at reducing the waiting time of inpatients so as to improve inpatients' satisfactions. In order to solve the complex treatment scheduling problem efficiently, we propose an approach based on a hybrid cuckoo search algorithm which is tested and validated in a real case. Moreover, home healthcare (HHC) is another real-world issue considering the aggravating trend of ageing population. In most areas, an increasing number of social-profit & non-profit organizations are joining in providing healthcare services to patients at their homes and it has a tendency to reach the hospital-level in both quantity and quality for the added flexibility than hospital's service. We investigate home healthcare scheduling and routing problem with consideration of many real-life factors, especially lunch break requirement. The problem is practical scenario motivated and aims at minimizing the total operating cost. We use the commercial solver Gurobi to solve and validate the model with real data
Modeling and Coordination of interconnected microgrids using distributed artificial intelligence approaches by Jin Wei( )

1 edition published in 2019 in English and held by 1 WorldCat member library worldwide

À mesure que les sources renouvelables pénètrent dans le système électrique actuel pour atténuer le réchauffement planétaire et la pénurie d'énergie, le concept de microréseau (MG) permet de réduire l'impact de la production intermittente sur le réseau de services publics. Il permet d'améliorer l'automatisation et l'intelligence du réseau électrique avec des caractéristiques plug-and-play. L'intégration d'un plus grand nombre de MG dans un réseau de distribution favorise le développement du réseau intelligent. Leur coordination pourrait conduire à une grande fiabilité du système avec un faible coût, et une forte résistance aux pannes électriques. La réalisation de ces profits repose sur des technologies développées de communication et de stratégies de contrôle.La répartition de la puissance dans les MG répartis tout en coordonnant les éléments au sein de chaque MG exige un contrôle décentralisé. L'approche multiagent permet de modéliser un réseau de MG comme un système physiquement distribué. Cette thèse étudie principalement le contrôle de coordination dans le réseau MG et sa modélisation à base d'agent.. L' objectif est de promouvoir la performance des contrôles en termes d'efficacité et de fiabilité. Deux méthodes sont envisagées pour permettre l'évolutivité du système, y compris la coordination avec les MG voisins et dans la zone de coordination étendue. Une plateforme de simulation est établie pour valider les approches proposées.Les stratégies de contrôle pour la coordination entre les MG et leurs voisins sont proposées afin de maintenir la charge complète et la sécurité tout en minimisant le coût de production. Le contrôle centralisé dans le groupe de coordination est appliqué à la gestion économique de l'énergie. Il utilise une méthode de Newton-Raphson pour répartir la puissance entre les MG voisins en simplifiant la relation entre le coût de production de MG et sa puissance de sortie. Une approche fondée sur le consensus est adoptée pour calculer le flux de puissance du réseau, et les résultats sont comparés avec la capacité maximale sur la ligne pour assurer un fonctionnement sûr. Pour améliorer encore les avantages économiques, l'approximation de la relation entre la puissance de production de MG et le coût de production est améliorée par une autre stratégie fondée sur la notion de marché. Il construit un marché pour le commerce d'électricité avec les voisins. Cette méthode préserve la vie privée de chaque MG. Le calcul du débit de puissance est simplifié pour être proportionnel à la différence d'angle entre les deux extrémités de la ligne de raccordement. Les deux stratégies sont testées sur plusieurs réseaux MG. Leur performance montre que les deux approches sont évolutives et pourraient économiquement compenser le manque d'approvisionnement en charge dans les MG défectueux.Pour la stratégie de contrôle avec une fiabilité et un profit plus élevés, une stratégie de coordination au sein d'une vaste zone sélectionnée de MG est proposée. L'élargissement de la zone de coordination en fonction des MG voisins fournit plus de sources d'énergie au MG. Il assure suffisamment de puissance pour compenser le déséquilibre et offre plus de choix pour la distribution de puissance. La sélection de la zone de coordination est réalisée par un algorithme évolutionnaire distribué. La programmation quadratique dans Gurobi est utilisée pour résoudre le problème de répartition de puissance. Un autre algorithme génétique est également adopté pour résoudre le problème de la répartition optimale de la puissance avec un coût de production quadratique pour la microturbine. La performance de cette stratégie est testée, et les résultats montrent qu'elle a des avantages en termes de fiabilité, d'évolutivité et de profit par rapport aux méthodes centralisées
Un modèle d'environnement pour la simulation multiniveau - Application à la simulation de foules by Jonathan Demange( )

1 edition published in 2012 in French and held by 1 WorldCat member library worldwide

Cette thèse propose un modèle organisationnel et holonique de l'environnement pour la simulation des déplacements de piétons dans des bâtiments. Une foule de piétons peut être considérée comme un système composé d'un grand nombre d'entités en interaction, dont la dynamique globale ne peut se réduire à la somme des comportements de ses composants. La simulation multiniveau fondée sur les modèles multiagents holoniques constitue une approche permettant d'analyser la dynamique de tels systèmes. Elle autorise leur analyse en considérant plusieurs niveaux d'observation (microscopique, mésoscopique et macroscopique) et prend en compte les ressources de calcul disponibles. Dans ces systèmes, l'environnement est considéré comme l'une des parties essentielles. La dynamique des piétons composant la foule est alors clairement distinguée de celle de l'environnement dans lequel ils se déplacent. Un modèle organisationnel décrivant la structure et la dynamique de l'environnement est proposé. L'environnement est structurellement décomposé en zones, sous-zones, etc. Les organisations et les rôles de cet environnement sont projetés dans une société d'agents ayant en charge de simuler la dynamique de l'environnement et les différentes missions qui lui sont classiquement assignées dans les systèmes multiagents. Ce modèle précise également les règles de passage entre deux niveaux d'observation. Ainsi, chaque agent appartenant au modèle de l'environnement tente d'utiliser une approximation des comportements de ses sous-zones afin de limiter la consommation de ressources durant la simulation. La qualité de l'approximation entre ces deux niveaux d'observation est évaluée avec des indicateurs énergétiques. Ils permettent de déterminer si l'agent approxime correctement les comportements des agents associés aux sous-zones. En sus du modèle organisationnel et holonique proposé, nous présentons un modèle concret de la simulation de voyageurs dans un terminal d'aéroport. Ce modèle concret est implanté sur les plateformes JaSIM et Janus
Contribution to robust and fiabilist optimization : Application to the design of acoustic radio-frequency filters by François Schott( )

1 edition published in 2019 in English and held by 1 WorldCat member library worldwide

This thesis aims to develop robust and fiabilist optimiztion means in order to face the future requirements of the radio-frequency (RF) filter market. The goals of this thesis are: to reduce the optimization process timespan, to be able to find a solution that fully satisfies a tough bill of specifications and to reduce failure rate due to manufacturing uncertainties. Several research works has been done to achieve these goals.During the formulation phase of an engineering design optimization (EDO) process, ambiguities, leading to unsatisfying solutions, could happen. In this case, some phases of the EDO process has to be iterated increasing its timpespan. Therefore, a Framework to properly formulate an optimization problem has been developed. During a run of optimization, for the algorithm to solve the problem according to designer preferences and thus avoid un-satisfying solution, two challenges, among others, have to be faced. The variable challenge is about handling mixed variables with different order of magnitudes while the satisfaction challenge is about properly computing satisfaction. The Normalized Evaluations Approach has been developed to face these challenges. The resolution method efficiency strongly relies on the choice of its core element: the algorithm. Hence, the high number of optimization algorithms is a challenge for an optimizer willing to choose the correct algorithm. To face this challenge, a Benchmark, being a tool to assess the algorithm performance and to be able to select the correct algorithm for a given problem, has been developed. Algorithm efficiency depends on the values given to its parameters, its setting. A common practice is to tune parameters manually which does not guarantee the best performance. A solution to this problem is to perform meta-optimization (MO) which consists in optimizing an algorithm efficiency by tuning its parameters. A MO approach using a benchmark to evaluate settings has been tested. A fiabilist optimization method, taking the uncertainties into account, has to be developed. However, this method has to do so without degrading resolution time, which is usually the case with fiabilist method. Therefore, a Sparing Fiabilist Optimization method taking uncertainties into account without increasing too much the numerical resolution timespan has been developed.These methods have been applied to optimize a RF filter, with a tough bill of specifications, for which no fully satisfying solution where found before the thesis. By using methods developed during this thesis, a determinist solution, not taking uncertainties into account, which fully satisfies the bill of specifications, has been found. Moreover, a fiabilist solution having a 71% success rate has been found. As a conclusion, it appears that optimization methods developed during these thesis where sufficient to face the future requirements of the radio-frequency filter market
Cellular matrix for parallel k-means and local search to Euclidean grid matching by Hongjian Wang( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

In this thesis, we propose a parallel computing model, called cellular matrix, to provide answers to problematic issues of parallel computation when applied to Euclidean graph matching problems. These NP-hard optimization problems involve data distributed in the plane and elastic structures represented by graphs that must match the data. They include problems known under various names, such as geometric k-means, elastic net, topographic mapping, and elastic image matching. The Euclidean traveling salesman problem (TSP), the median cycle problem, and the image matching problem are also examples that can be modeled by graph matching. The contribution presented is divided into three parts. In the first part, we present the cellular matrix model that partitions data and defines the level of granularity of parallel computation. We present a generic loop for parallel computations, and this loop models the projection between graphs and their matching. In the second part, we apply the parallel computing model to k-means algorithms in the plane extended with topology. The proposed algorithms are applied to the TSP, structured mesh generation, and image segmentation following the concept of superpixel. The approach is called superpixel adaptive segmentation map (SPASM). In the third part, we propose a parallel local search algorithm, called distributed local search (DLS). The solution results from the many local operations, including local evaluation, neighborhood search, and structured move, performed on the distributed data in the plane. The algorithm is applied to Euclidean graph matching problems including stereo matching and optical flow
Agent-based model for the rescheduling of Individual and collective daily activities under uncertainties by Hui Zhao( )

1 edition published in 2020 in English and held by 1 WorldCat member library worldwide

Daily activity schedule are popular for people duringdaily life. While, when executing the schedule on the real road network, there are always some disruptions disturbing the planned schedule. To deal with this problem, daily activity rescheduling is necessary. This thesis regards the disruptions from the activity schedule execution environment as unexpected events (uncertainties). It establishes agent-based models to simulate the activity rescheduling decision process from the aspects of individual activity rescheduling and joint trip renegotiating.For the individual activity rescheduling, the model in this thesis wants to explore the relationship between a pair of episodes (two connected episodes) under unexpected events. Therefore, activity type is an important factor to consider. This thesis uses the decision tree to search all the alternative choices, and then it calculates the penalty after applying for the choices for each episode. For the joint trip renegotiating problem, when unexpected events happens, such as congestion, the driver and passenger need to renegotiate the drop off place and arrival time. The passenger drop off place may be a place near to his original location, or a new location. This thesis proposes a tolerance distance to find the alternative drop off place, and it uses the utility function to calculate the score of each alternative choice. Also, during the renegotiation process, this thesis considers the relationship between the passenger and driver, and also the time pressure. Both of them affect the person's concession degree to his opponent.The goal of this thesis is to simulate the activity rescheduling decision and its focus is the travel behavior. It defines the unexpected events that may occur during the activity schedule execution process, and it establishes models to deal with both the individual activity rescheduling decision-making process and joint trip renegotiating process. It would like to provide a method to simulate the rescheduling decision-making mostly closed to the reality, while, it still needs to be validated to the real case in the near future
Algorithmes inspirés des sociales pour concevoir des nouveaux systèmes de pilotage dans le contexte de l'usine du futur by Tsegay Tesfay Mezgebe( )

1 edition published in 2020 in English and held by 1 WorldCat member library worldwide

The use of traditionally centralized control system does not able to meet the rapidly changing customer expectations, high product varieties, and shorter product life-cycles. In particular, the emergence of Cyber Physical System (CPS) which can be seen as interacting networks of physical and computational components has provided the foundation for many new factories' infrastructures and improved the quality of products and processes. This Cyber Physical System has dramatically impacted the centrally predictive control system in responding to perturbation(s) in the current dynamic market characteristics. Urgent change for example is one of the common perturbations and has significant perturbing ability to a central predictive control system. Accordingly, it is now accepted that using agent-based control system improves the reactivity to treat these perturbation(s) until they are no longer limiting factors. In this study, the use of human-inspired interaction approach (by means of negotiation and consensus-based decision-making algorithms) is explored to design and propose a new control system. It has taken advantages from Industry 4.0 assets and encompassed heterogeneous and intelligent entities (mainly the product entities and resource entities) and discrete event systems. Each entity could have different capability (evolution, learning, etc.) and the whole physical and control system may lead emerging behaviors to dynamically adapt the perturbation(s). Hence, every intelligent entity decides when to broadcast its current state to neighbor entities and the controlling decision depends on the behavior of this state. The negotiation and consensus-based decision-making algorithms were initially formulated and modeled by networking all the contributing and heterogeneous entities considering two real industrial shop floors. Then after, simulation and application implementation tests on the basis of full-sized academic platform called TRACILOGIS platform have been conducted to verify and validate these decision-making algorithms. This has been done with expectations that the applicability of these algorithms will be more adaptable to set best priority-based product sequencing and rescheduling than another decision-making approach called pure reactive control approach. Accordingly, the experimental results have shown that the negotiation and consensus among the decisional entities have significantly minimized the impact of perturbation(s) on a production process launched on the TRACILOGIS platform. Meanwhile, using these two decision-making algorithms has conveyed better global performance (e.g., minimized makespan) over the pure reactive control approach
 
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Swarm Intelligence Based Optimization Second International Conference, ICSIBO 2016, Mulhouse, France, June 13-14, 2016, Revised Selected Papers
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Artificial evolution : 14th International Conference, Évolution Artificielle, EA 2019, Mulhouse, France, October 29-30, 2019, revised selected papers
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