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2018Abir Chaabani, Slim Bechikh, Lamjed Ben Said
A new co-evolutionary decomposition-based algorithm for bi-level combinatorial optimization
Applied Intelligence, 48(9), 2847-2872, 2018
Abstract
Bi-Level Optimization Problems (BLOPs) are a class of challenging problems with two levels of optimization tasks. The main goal is to optimize the upper level problem which has another optimization problem as a constraint. The latter is called the lower level problem. In this way, the evaluation of each upper level solution requires finding an (near) optimal solution to the corresponding lower level problem, which is computationally very expensive. Many real world applications are bi-level by nature, ranging from logistics to software engineering. Further, proposed bi-level approaches have been restricted to solve linear BLOPs. This fact has attracted the evolutionary computation community to tackle such complex problems and many interesting works have recently been proposed. Unfortunately, most of these works are restricted to the continuous case. Motivated by this observation, we propose in this paper a new Co-evolutionary Decomposition Algorithm inspired from Chemical Reaction Optimization algorithm, called E-CODBA (Energy-based CODBA), to solve combinatorial bi-level problems. Our algorithm is based on our previous works within this research area. The main idea behind E-CODBA is to exploit co-evolution, decomposition, and energy laws to come up with good solution(s) within an acceptable execution time. The statistical analysis of the experimental results on the Bi-level Multi-Depot Vehicle Routing Problem (Bi-MDVRP) show the out-performance of our E-CODBA against four recently proposed works in terms of effectiveness and efficiency.
Abir Chaabani, Lamjed Ben SaidHybrid CODBA-II Algorithm Coupling a Co-Evolutionary Decomposition-Based Algorithm with Local Search Method to Solve Bi-Level Combinatorial Optimization
International Conference on Tools with Artificial Intelligence ICTAI’18, Volos, 2018
Abstract
Bi-level optimization problems (BLOPs) are a class of challenging problems with two levels of optimization tasks. The usefulness of bi-level optimization in designing hierarchical decision processes prompted several researchers, in particular the evolutionary computation community, to pay more attention to such kind of problems. Several solution approaches have been proposed to solve these problems; however, most of them are restricted to the continuous case. Motivated by this observation, we have recently proposed a Co-evolutionary Decomposition-based Algorithm (CODBA-II) to solve combinatorial bi-level problems. CODBA-II scheme has been able to improve the bi-level performance and to bring down the computational expense significantly as compared to other competitive approaches within this research area. In this paper, we present an extension of the recently proposed CODBA-II algorithm. The improved version, called CODBA-IILS, further improves the algorithm by incorporating a local search process to both upper and lower levels in order to help in faster convergence of the algorithm. The improved results have been demonstrated on two different sets of test problems based on the bi-level production-distribution problems in supply chain management, and comparison results against the contemporary approaches are also provided.
Thouraya Sakouhi, , ,Immersive Analytics for Floods Management Semantic Trajectory Data Warehouse Ontology
Immersive Analytics for Floods Management Semantic Trajectory Data Warehouse Ontology. iLRN 2018 Montana, 169., 2018
Abstract
Semantic Immersive Analytics is a new paradigm that has the capability for visualizing ontologies and meta-data including annotated web-documents, images, and digital media such as audio and video clips in a synthetic three-dimensional semi-immersive environment. More importantly, it supports visual semantic analytics, whereby an analyst can interactively investigate complex relationships between heterogeneous information and supports query processing and semantic association discovery. In our previous work we proposed a Semantic Trajectory Data Warehouse Ontology (STrDWO) [15], a tool supporting designers at the modeling of ontology-based trajectory data warehouses. In here, we intend to integrate our aforementioned tool with augmented Reality (AR) technologies to provide multi-sensory interfaces that support collaboration and allow users to immerse themselves in their data in a way that supports real-world geo-space analytics tasks. To do so, we present a Semantic trajectory data warehouse having an ontology-based multidimensional model. We illustrate our approach by a case study dealing with floods management.
, Ameni Azzouz, ,Solving energy ordering problem with multiple supply-demand using Bilevel optimization approach
Procedia Computer Science, 130, 753-759., 2018
Abstract
We develop in this paper an energy ordering problem with multiple energy supplying sources and multiple traders trying to satisfy customers’ demands. Such a supply chain network is split of three main layers: the set of energy generation plants (suppliers), a set of traders trying to expect and satisfy customer’s demands dispatched. Following the new investment in renewable energy, customers have the option to choose the nature of its electricity. Customer choice has an impact on the future energy supply chain. For that, we deal with the customer choice in our considered problem. Motivated by this architecture, we propose an evolutionary algorithm-based on bi-level optimization model is developed to handle this problem. The performance of the proposed model is evaluated by numerical experiments based on real-world data.
, Wassim Ayadi,NBF: an fca-based algorithm to identify negative correlation biclusters of DNA microarray data
IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA) Pages 1003-1010, 2018
Abstract
Biclustering is a popular technique to study gene expression data, especially to identify functionally related groups of genes under subsets of conditions. Nevertheless, most of the existing biclustering algorithms only focus on the positive correlations of genes. However, recent research shows that groups of biologically significant genes may exhibit negative correlations. Thus, we need a novel way to efficiently unveil such a type of correlations. We introduce, in this paper, a new algorithm, called the Negative Bicluster Finder (NBF). The sighting features of the NBF stands in its ability to discover the biclusters of negative correlations using the theoretical results provided by the Formal Concept Analysis. Exhaust experiments are carried out on three real-life datasets to assess the performance of the NBF. Our results prove the NBF’s ability to statistically and biologically identify significant biclusters.
, Ameni Azzouz, Meriem EnnigrouSolving Flexible Job Shop Scheduling Problem using Hybrid Bilevel Optimization model
HIS 2018, 2018
Abstract
Flexible Job Shop Problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. This problem is decomposed into two sub-problems: the assignment problem and the scheduling problem. Following this structure, we consider in this work the FJSP as a bilevel problem. For that, we are interested to solve this problem with bilevel optimization method in which the upper level optimizes the assignment problem and the lower level optimizes the scheduling problem. Therefore, we propose, for the first time, an hybrid bilevel optimization model named HB-FJSP based on both exact and approximate methods to solve the FJSP in order to minimize the makespan. The computational results confirm that our model HB-FJSP provides better solutions than other models.
Samira Harrabi, Ines Ben Jaafar,A Swarm Intelligence-based Routing Protocol for Vehicular Networks
International Journal of Vehicle Information and Communication Systems (IJVICS), 2018
Abstract
Vehicular Ad hoc Networks (VANETs) are a particular case of Mobile Ad hoc Networks (MANETs). They are applied to exchange information among vehicles and between vehicles and a nearby fixed infrastructure. Unlike the MANETs, the VANETs have highly mobile nodes that cause a dynamic topology, a disconnected network, etc. Consequently, these features pose numerous challenges. One of them is routing. In a vehicular environment, the routing protocol needs to cope with events like link failure and to find an effective path to propagate the information toward the desired destination. In this context, we assume in this paper that the vehicles are intelligent and have a knowledge base about their communication environment. Our aim is to carry out the routing of the data based on swarm intelligence. The optimum route is explored using the Particle Swarm Optimisation (PSO). The proposed approach is called the Optimised Agent-based AODV Protocol for VANET (OptA2PV).
Hanen Lejmi,Emotions recognition in an intelligent elearning environment.
Emotions recognition in an intelligent elearning environment., 2018
Abstract
For the purpose of improving the quality in Elearning process and overcoming the limitations of the current online educational environments, we propose to take into consideration the emotional states of students during Elearning sessions. Our objective is to ensure the ability of emotional intelligence: Emotion Recognition, in an eLearning environment. Thus, we present an architecture of Emotionally Intelligent Elearning System (EIES). Within the development of a computational probabilistic model of emotions, we proposed a Bayesian Network (BN) model to deal with emotions in Elearning environments and handle the uncertain nature of emotion recognition process. In a second phase, we focus on the incorporation of the emotion recognition in the Elearning systems by developing a simulation of EIES based on the BN model, able to predict the students’ affects. Consequently, we reached positive and promising results related to the fact that simulated EIES based on the BN model of emotions predicts correctly the student’s emotion when an event occurs during an Elearning session.
Hamida Labidi, ,Genetic Algorithm for Solving a Dynamic Vehicle Routing Problem with Time Windows
This paper proposes a genetic algorithm to solve the dynamic vehicle routing problem with time windows, presented at HPCS 2018., 2018
Abstract
The Vehicle Routing Problem (VRP) introduced by Dantzing and Ranser (1959) is a prominent combinatorial optimization problem. Over the last several decades, many variants of the multi-constrained vehicle routing problem have been studied and a class of problems known as rich vehicle routing problem (RVRPs), has been formed. This work is about solving a variant of RVRP with dynamically changing orders and time windows constraints. In the real world application, during the working day, new orders often occur dynamically and need to be integrated into the routes planing. A Genetic Algorithm (GA) with a simple heuristic is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on Solomon’s benchmark with different percentage of the orders revealed to the algorithm during operation time.
Hamdi Ouechtati, Nadia Ben Azzouna, Lamjed Ben SaidTowards a self-adaptive access control middleware for the Internet of Things
In 2018 International Conference on Information Networking (ICOIN) (pp. 545-550). IEEE., 2018
Abstract
In order to cope with certain challenges posed by IoT environment and device capacity, a Self-Adaptive access control model is needed to resolve security and privacy issues. The use of complex encryption algorithms is infeasible due to the volatile nature of IoT environment and pervasive devices with limited resources. In this paper, we propose an access control middleware for the Internet of Things. The latter is an extension of the ABAC model in order to take into account the subject behavior and the trust value in the decision making process. In this work, we introduce a dynamic adaptation process of access control rules based on the risk value, the policies and rule sets which can effectively improve the security of IoT applications and produce more efficient access control mechanisms for the Internet of Things.
