Karima Benatchba

Using artificial bees to solve partitioning and scheduling problems in codesign

Partitioning and scheduling are two central issues in the design of embedded systems since they can widely influence the characteristics of the system under design. The numerous constraints imposed by the environment and/or the underlying target architecture of mixed systems (containing hardware and software parts) make these two problems hard to solve. This paper introduces an automatic approach that integrates simultaneously partitioning and scheduling. It is inspired by the collective behavior of social insects such as bees in order to find a feasible solution to partitioning, using scheduling to find the shortest execution time to the system under design.

A new weighted shortest path tree for convergecast traffic routing in WSN

Tree topologies are widely used in WSN in order to route convergecast traffic to the sink. We consider in this paper the Shortest Path routing Tree (SPT) problem in WSN under different metrics; we show that the basic SPT based strategies are unsuitable for the many-to-one WSN when considering some metrics to compute link costs. Indeed, existing SPT approaches aim to construct a tree rooted at the sink such that the cost of the path from any node to the sink is minimal, while the cost of a given path is computed as summation of the costs of links that compose this path. However, in many-to-one WSN, links which are close to the sink are more critical than other links when using some metrics. We propose in this paper a new weighted path cost function, and we show that our cost function is more suitable for WSN. Based on this cost function, we propose a simple and efficient weighted shortest path tree construction which does not introduce new overheads. We consider, then, the particular case of energy-aware routing in WSN when we apply our new solution in order to construct more suitable energy-aware SPT. We conduct extensive simulations which show that our approach allows to enhance the network lifetime up to 17% compared to the basic one.

AntPart : an algorithm for the unsupervised classification problem using ants

Unsupervised classification is one of the tasks of data-mining. In this paper, a method named AntPart for the resolution of exclusive unsupervised classification is introduced. It is inspired by the behavior of a particular species of ants called Pachycondyla apicalis. The performances of this method are compared with those of three other ones, also inspired by the social behavior of ants: AntClass, AntTree and AntClust.

Image segmentation using quantum genetic algorithms

On one hand, image segmentation is a low-level processing task which consists in partitioning an image into homogeneous regions. It can be seen as being a combinatorial optimization problem. In fact, considering the huge amount of information that an image carries, it is impossible to find the best segmentation. On the other hand, quantum genetic algorithms are characterized by their high diversity, and by a good balance between global and local search. In this paper, we present a quantum genetic algorithm for image segmentation

A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions

Abstract The Maximum Power Point Tracking controller (MPPT) is a key element in Photovoltaic systems (PV). It is used to maintain the PV operating point at its maximum under different temperatures and sunlight irradiations. The goal of a MPPT controller is to satisfy the following performances criteria: accuracy, precision, speed, robustness and handling the partial shading problem when climatic changes variations occur. To achieve this goal, several techniques have been proposed ranging from conventional methods to artificial intelligence and bio-inspired methods. Each technique has its own advantage and disadvantage. In this context, we propose in this paper, a new Bio- inspired MPPT controller based on the Ant colony Optimization algorithm with a New Pheromone Updating strategy (ACO_NPU MPPT) that saves the computation time and performs an excellent tracking capability with high accuracy, zero oscillations and high robustness. First, the different steps of the design of the proposed ACO_NPU MPPT controller are developed. Then, several tests are performed under standard conditions for the selection of the appropriate ACO_NPU parameters (number of ants, coefficients of evaporation, archive size, etc.). To evaluate the performances of the obtained ACO_NPU MPPT, in terms of its tracking speed, accuracy, stability and robustness, tests are carried out under slow and rapid variations of weather conditions (Irradiance and Temperature) and under different partial shading patterns. Moreover, to demonstrate the superiority and robustness of the proposed ACO_NPU_MPPT controller, the obtained results are analyzed and compared with others obtained from the Conventional Methods (P&O_MPPT) and the Soft Computing Methods with Artificial intelligence (ANN_MPPT, FLC_MPPT, ANFIS_MPPT, FL_GA_MPPT) and with the Bio Inspired methods (PSO) and (ACO) from the literature. The obtained results show that the proposed ACO_NPU MPPT controller gives the best performances under variables atmospheric conditions. In addition, it can easily track the global maximum power point (GMPP) under partial shading conditions.

Solving partitioning problem in codesign with ant colonies

Partitioning problem in codesign is of great importance since it can widely influence the characteristics of the system under design. The numerous constraints imposed by the environment and/or the underlying target architecture, in addition to its NP-Completeness makes the problem hard to solve. This paper introduces an automatic partitioning approach inspired by the collective behavior of social insects such as ants, which are able to find the shortest path from their nest to a food source.

Efficient distributed discovery and composition of OWL-S process model in P2P systems

AbstractSemantic Web services (SWs) has become the most dominant paradigm of the service-oriented computing and one of the hot issues in the area of distributed computing technology to perform business services composition more efficiently and effectively for a number of years now. The distributed composition of SWs according to their functionality increases the capability of an application to fulfill the user’s requirements. In this paper, we describe an efficient approach for improving the performance and effectiveness of automatic and cooperative composition of SWs in P2P systems. It implements a distributed solution based on scalable epidemic algorithm to discover and compose SWs in P2P systems. The main idea of our approach is to develop hybrid matching technique that operates on OWL-S process models in order to ensure high recall, further reduce the number of messages exchanged and reduce the execution time for discovering and composing SWs in the P2P network. Moreover, our matching technique is able to detect complex matching between these SWs based on their parameters and the user request. We propose a similarity measure that will be used to compose new discovered and heterogeneous collaborative Web services of large-scale distributed systems in a P2P network for satisfying user requirements, and to rank the results according to a similarity score expressing the affinities between each of them and a user-submitted query. The experimental results show that our approach is efficient and able to reduce considerably the execution time and message overhead, while preserving high levels of the distributed discovery and composition of SWs on large-size P2P networks.

Ranked Matching of OWL-S Process Model for Distributed Discovery of SWs in P2P Systems

Semantic Web services (SWs) and P2P computing have emerged as new paradigms for solving complex problems by enabling large-scale aggregation and sharing of distributed computational resources. In this paper, we present a scalable approach based on epidemic discovery algorithm to discover new distributed and heterogeneous collaborative applications of large-scale distributed systems in a P2P network, and to rank the results according to a similarity score expressing the affinities between each of them and a user-submitted query. In order to reduce the execution time and improve the applicability of the epidemic discovery algorithm for discovering SWs, we propose the matching of ontology OWL-S process model in the heart of this algorithm which reduces the search space while keeping an acceptable matching quality level. Moreover, our matching approach is able to detect complex mappings between OWL-S process models based on their parameters. Experiments showed that the matching technique reduces considerably the execution time, maintaining at the same time a good quality of the distributed discovery of SWs in a P2P network.

Using Genetic Algorithms for solving partitioning problem in codesign

Partitioning problem in codesign is of critical importance since it has big impact on cost/performance characteristics of the final product. Tt is an NP-Complete problem that deals with the different constraints relative to the system and the underlying target architecture. The reported partitioning approaches have several drawbacks (they are often dedicated to a particular application or target architecture, they operate at a unique granularity level, most of them are manual and impossible to apply for complex systems, the number of constraints they deal with is generally limited...). This paper introduces an automatic approach using genetic algorithms to solve partitioning in codesign. This approach is totally independent of target architecture. Another advantage of this approach is that it allows determining dynamically the granularity of the objects to partition, making it possible to browse more efficiently solution space.

Hybrid Ontology-Based Matching for Distributed Discovery of SWS in P2P Systems

Semantic Web services (SWs) paradigm is considered as the most dominant technology of the Service-Oriented Computing (SOC). SWs have emerged as a major technology for deploying automated interactions between distributed and heterogeneous applications. This computing technology can be used to discover new distributed and heterogeneous collaborative applications of large-scale distributed systems in P2P systems. In this paper, we present a scalable P2P approach for distributed discovery of SWs. In this approach, we define a distributed solution based on epidemic discovery algorithm to achieve a specific goal through the distributed discovery of SWs in P2P networks. In order to improve the applicability of the epidemic discovery algorithm for discovering SWs, we propose the matching of ontology OWL-S in the heart of this algorithm which reduces the search space while keeping an acceptable matching quality level. Our matching approach relies on the use of several similarity metrics. Moreover, our matching approach is able to detect complex mappings between activities based on their parameters of OWL-S.