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Moez Hammami

Informations générales

Moez Hammami
Grade

Maître Assistant

Biographie courte

Moez Hammami is an Assistant Professor in the higher institute of management of Tunis. He has a PHD in computer science for management from the higher school of management of Tunis. He has twenty years of experience in teaching and research in the most prestigious universities and laboratories in Tunisia such us the National school of computer science of the University of Manouba which is considered as the best computer science engineering school in Tunisia and the higher institute of management of Tunis which is the best school of management having an IT department specified in computer science applied to management. He taught several subjects such as the theory of languages and automata, compiler techniques, theory of complexity of algorithms, web programming, and combinatorial optimization covering exact and approximate methods. He has published many papers in the domain of combinatorial optimization and specially meta-heuristics and hyper-heuristics.  He also participated in the foundation of the research laboratory SOIE (now SMART), and the Tunisian association of artificial intelligence, in which he held the position of treasurer and within the framework of which he participated in the organization of several national and international conferences

Équipes

Axes de recherche

Evolutionary optimization
Multi-Objective Optimization
Soft Computing
Optimization
Computer Science
Multi Agent System

Publications

  • 2023
    Imen Oueslati, Moez Hammami, Issam Nouaouri, Ameni Azzouz, Lamjed Ben Said, Hamid Allaoui

    A Honey Bee Mating Optimization HyperHeuristic for Patient Admission Scheduling Problem

    In proceedings of The 9th International Conference on Metaheuristics and Nature Inspired Computing META Marrakech, Nov 01-04, 2023, 2023

    Résumé

    Hyperheuristics represent a generic method that provides a high level of abstraction, enabling solving several problems in the combinatorial optimization domain while reducing the need for human intervention in parameters tuning. This category consists in managing a set of low-level heuristics and attempting to find the optimal sequence that produces high-quality results. This paper proposes a hyperheuristic that simulates the honey bees mating behavior called “Honey bee Mating Optimization HyperHeuristic”  to solve the Patient Admission Scheduling Problem (PASP). The PASP is an NP-hard problem that represents an important field in the health care discipline. In order to perceive the influence of low-level heuristics on the model’s performance, we implemented two versions of the hyperheuristic that each one works on a different set of low-level heuristics. The results show that one of the versions generates better results than the other, revealing the important role of low-level heuristics’ quality leading to enhancing the hyperheuristic performance.

  • Khaoula Bouazzi, Moez Hammami, Sadok Bouamama

    Application of an improved genetic algorithm to Hamiltonian circuit problem

    Procedia Computer Science Volume 192, 2021, Pages 4337-4347, 2021

    Résumé

    In the last few years, there has been an increasing interest in Random Constraint Satisfaction Problems (CSP) from both experimental and theoretical points of view. To consider a variant instance of the problems, we used a random benchmark. In the present paper, some work has been done to find the shortest Hamiltonian circuit among specified nodes in each superimposed graph (SGs). The Hamiltonian circuit is a circuit that visits each node in the graph exactly once. The Hamiltonian path may be constructed and adjusted according to specific constraints such as time limits. A new constraint satisfaction optimization problem model for the circuit Hamiltonian circuit problem in a superimposed graph has been presented. To solve this issue, we propose amelioration for the genetic algorithm using Dijkstra’s algorithm, where we create the improved genetic algorithm (IGA). To evaluate this approach, we compare the CPU and fitness values of the IGA to the results provided by an adapted genetic algorithm to find the shortest Hamiltonian circuit in a superimposed graph.

    Imen Oueslati, Moez Hammami

    Honey Bee Cooperative HyperHeuristic

    special issue: Knowledge- Based and Intelligent Information and Engineering Systems: Proceedings of the 25th International Conference KES2021 Volume 192, 2021, Pages 2871-2880, 2021

    Résumé

    Hyperheuristics form a new concept that provides a more general procedure for optimization. Their goal is to manage existing low-level heuristics to solve a large number of problems without specific parameter tuning.
    In this paper, we propose three hyperheuristics based on honey bees behaviour: ”Bee colony optimization HyperHeuristic” BCOH2, ”Honey bee Mating Optimization HyperHeuristic” HBMOH2 and ”Honey Bee Cooperative HyperHeuristic” HBCH2 which cooperates between the two mentioned hyperheuristics. The proposed hyperheuristics are implemented under the Hyflex platform. Tested on the MAX-SAT and the Bin Packing problems, our algorithms showed good results compared to hyperheuristics participating in the CHeSC competition.
  • khaoula Bouazzi, Moez Hammami, Sadok bouamama

    Hybrid Genetic Algorithm for CSOP to Find the Lowest Hamiltonian Circuit in a Superimposed Graph

    Artificial Intelligence and Soft Computing, Springer International Publishing, 2019, pp 512--525, 2019

    Résumé

    Many fields use the graphs as a tool of representation such as multimodal networks, computer networks, wireless sensor networks, energy distribution. But, beyond the representation of data, the graphs also serve to propose solutions to certain problems mentioning the well-known problem finding the shortest Hamiltonian circuit in a graph. The aim of this paper is to elucidate a mechanism to obtain the most efficient Hamiltonian circuit among specified nodes in a given superimposed graphs (SGs). The Hamiltonian circuit is a circuit that visits each node on the graph exactly once. The SG represents a scheme of multimodal transportation systems and takes into account distance among other variables. The Hamiltonian path may be constructed and adjusted according to specific constraints such as time limits. This paper introduces new constraint satisfaction optimization problem formalism (CSOP) for the problem of finding the lowest Hamiltonian circuit in superimposed graphs, and as a resolution method, we use the genetic algorithm. As a case study, we adopt the transportation data of Guangzhou, in China.

    Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled Ghedira

    A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Drift

    book-chapter in learning from data streams in evolving environments, pp 39-61. Springer International Publishing, January 2019., 2019

    Résumé

    Recent advances in Computational Intelligent Systems have focused on addressing complex problems related to the dynamicity of the environments. Generally in dynamic environments, data are presented as streams that may evolve over time and this is known by concept drift. Handling concept drift through ensemble classifiers has received a great interest in last decades. The success of these ensemble methods relies on their diversity. Accordingly, various diversity techniques can be used like block-based data, weighting-data or filtering-data. Each of these diversity techniques is efficient to handle certain characteristics of drift. However, when the drift is complex, they fail to efficiently handle it. Complex drifts may present a mixture of several characteristics (speed, severity, influence zones in the feature space, etc.) which may vary over time. In this case, drift handling is more complicated and requires new detection and updating tools. For this purpose, a new ensemble approach, namely EnsembleEDIST2, is presented. It combines the three diversity techniques in order to take benefit from their advantages and outperform their limits. Additionally, it makes use of EDIST2, as drift detection mechanism, in order to monitor the ensemble’s performance and detect changes. EnsembleEDIST2 was tested through different scenarios of complex drift generated from synthetic and real datasets. This diversity combination allows EnsembleEDIST2 to outperform similar ensemble approaches in terms of accuracy rate, and present stable behaviors in handling different scenarios of complex drift.

  • Imen Khammamssi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled Ghedira

    Discussion and review on evolving data streams and concept drift adapting

    Evolving Systems, An Interdisciplinary Journal for Advanced Science and Technology Volume 9, pages 1–23, (2018), 2016

    Résumé

    Recent advances in computational intelligent systems have focused on addressing complex problems related to the dynamicity of the environments. In increasing number of real world applications, data are presented as streams that may evolve over time and this is known by concept drift. Handling concept drift is becoming an attractive topic of research that concerns multidisciplinary domains such that machine learning, data mining, ubiquitous knowledge discovery, statistic decision theory, etc... Therefore, a rich body of the literature has been devoted to the study of methods and techniques for handling drifting data. However, this literature is fairly dispersed and it does not define guidelines for choosing an appropriate approach for a given application. Hence, the main objective of this survey is to present an ease understanding of the concept drift issues and related works, in order to help researchers from different disciplines to consider concept drift handling in their applications. This survey covers different facets of existing approaches, evokes discussion and helps readers to underline the sharp criteria that allow them to properly design their own approach. For this purpose, a new categorization of the existing state-of-the-art is presented with criticisms, future tendencies and not-yet-addressed challenges.

  • Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled Ghedira

    Self-Adaptive Windowing Approach for Handling Complex Concept Drift

    Cognitive Computation Journal, Springer. vol.7, pages 772–790, issue.6 (2015), Evolving Systems, Springer-Verlag Berlin Heidelberg 2016, 2015

    Résumé

    Detecting changes in data streams attracts major attention in cognitive computing systems. The challenging issue is how to monitor and detect these changes in order to preserve the model performance during complex drifts. By complex drift, we mean a drift that presents many characteristics in the sometime. The most challenging complex drifts are gradual continuous drifts, where changes are only noticed during a long time period. Moreover, these gradual drifts may also be local, in the sense that they may affect a little amount of data, and thus make the drift detection more complicated. For this purpose, a new drift detection mechanism, EDIST2, is proposed in order to deal with these complex drifts. EDIST2 monitors the learner performance through a self-adaptive window that is autonomously adjusted through a statistical hypothesis test. This statistical test provides theoretical guarantees, regarding the false alarm rate, which were experimentally confirmed. EDIST2 has been tested through six synthetic datasets presenting different kinds of complex drift, and five real-world datasets. Encouraging results were found, comparing to similar approaches, where EDIST2 has achieved good accuracy rate in synthetic and real-world datasets and has achieved minimum delay of detection and false alarm rate.

    Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled ghedira

    Self-Adaptive Windowing Approach for Handling Complex Concept Drift

    Cognitive Computation Journal 7, 772–790 (2015). https://doi.org/10.1007/s12559-015-9341-0, 2015

    Résumé

    Detecting changes in data streams attracts major attention in cognitive computing systems. The challenging issue is how to monitor and detect these changes in order to preserve the model performance during complex drifts. By complex drift, we mean a drift that presents many characteristics in the sometime. The most challenging complex drifts are gradual continuous drifts, where changes are only noticed during a long time period. Moreover, these gradual drifts may also be local, in the sense that they may affect a little amount of data, and thus make the drift detection more complicated. For this purpose, a new drift detection mechanism, EDIST2, is proposed in order to deal with these complex drifts. EDIST2 monitors the learner performance through a self-adaptive window that is autonomously adjusted through a statistical hypothesis test. This statistical test provides theoretical guarantees, regarding the false alarm rate, which were experimentally confirmed. EDIST2 has been tested through six synthetic datasets presenting different kinds of complex drift, and five real-world datasets. Encouraging results were found, comparing to similar approaches, where EDIST2 has achieved good accuracy rate in synthetic and real-world datasets and has achieved minimum delay of detection and false alarm rate.

    Hammadi Ghazouani, Moez Hammami, Ouajdi Korbaa

    Solving airport gate assignment problem using Genetic Algorithms approach

    2015 4th International Conference on Advanced Logistics and Transport (ICALT) pp 175-180 Valenciennes, France, 2015

    Résumé

    Because of the rapid growth of air traffic, optimizing airport management is becoming necessary in order to improveairport's capacity and better align its resources to the received traffic. In this paper we study the assignment of the arriving aircrafts to the available gates using the fixed daily schedule. We introduce a new approach based on Genetic Algorithms (GA) to solve the gate assignment problem (GAP). The encoding strategy consists in representing the chromosome by a vector of integers. The index of each gene represents the flight number and its value represents the gate to which the flight will be assigned. The method used to generate the initial population is based on three different heuristics and a random sorting of the gates. The selection method is the “In fitness proportionate selection” known as “roulette wheel selection”. In addition to one point and two point Crossover operators, we designed a Greedy procedure Crossover (GPX) operator. The experimentation is based on the use of fictive scenarios generated in accordance with the physical characteristics of the Tunis Carthage Airport and using different flight schedules. The comparison between deterministic approach, simple heuristics and the GA has shown the efficiency of the last approach in terms of solution's quality when we aim at solving the problems of large size. In order to determine the best configuration of the GA, we compared the different crossover operators and we noticed that the use of GPX improves the speed of convergence of the algorithm towards better solutions.

  • Hammadi Ghazouani, Moez Hammami, Ouajdi Korbaa

    Ensemble classifiers for drift detection and monitoring in dynamical Environments

    Annual Conference of the Prognostics and Health Management Society 2013, 2013

    Résumé

    Detecting and monitoring changes during the learning process are important areas of research in many industrial applications. The challenging issue is how to diagnose and analyze these changes so that the accuracy of the learning model can be preserved. Recently, ensemble classifiers have achieved good results when dealing with concept drifts. This paper presents two ensembles learning algorithms BagEDIST and BoostEDIST, which respectively combine the Online Bagging and the Online Boosting with the drift detection method EDIST. EDIST is a new drift detection method which monitors the distance between two consecutive errors of classification. The idea behind this combination is to develop an ensemble learning algorithm which explicitly handles concept drifts by providing useful descriptions about location, speed and severity of drifts. Moreover, this paper presents a new drift diversity measure in order to study the diversity of base classifiers and see how they cope with concept drifts. From various experiments, this new measure has provided a clearer vision about the ensemble’s behavior when dealing with concept drifts.

    imen khamassi, Mohamed Sayed Mouchaweh, Moez Hammami

    Nouvelle méthode de détection de dérive basée sur la distance entre les erreurs de classification

    5e Journées Doctorales Journées Nationales MACS, Strasbourg : France (2013), 2013

    Résumé

    La classification dynamique s’intéresse au traitement des données non-stationnaires issues des environnements évolutifs dans le temps. Ces données peuvent présenter des dérives, qui affectent la performance du modèle d’apprentissage initialement construit. Aujourd’hui, beaucoup d’intérêts sont portés sur la surveillance, la mise à jour et le diagnostic de ces dérives afin d’améliorer la performance du modèle d’apprentissage. Dans ce contexte, une nouvelle méthode de détection de dérive basée sur la distance entre les erreurs de classification est présentée. Cette méthode, nommée EDIST, surveille la distribution des distances des erreurs de classification entre deux fenêtres de données afin de détecter une différence à travers un test d’hypothèse statistique. EDIST a été testée à travers des bases de données artificielles et réelles. Des résultats encourageants ont été trouvés par rapport à des méthodes similaires. EDIST a pu trouver les meilleurs taux d’erreur de classification dans la plupart des cas et a montré une robustesse envers le bruit et les fausses alarmes.