Hadoop is an open-source framework that is widely used to store and process large amounts of data. Its core component is called YARN that is responsible for resource management and job scheduling for Big Data applications. Although YARN has very advanced processing performance. It suffers from resource allocation problems when used in a heterogeneous cluster serving multiple users. To deal with this heterogeneity and overcome the degradation of cluster, load balancing has become a major challenge for Hadoop run-time management. In this paper, an efficient resource allocation strategy combining multi-criteria decision-making and accurate clustering of nodes in a heterogeneous Hadoop environment is proposed. A multi-criteria decision approach relying on a modified analytical hierarchy process method is applied to assign a score to jobs depending on the resources requirements at run time. First, the proposed system profiles the available nodes by grouping them into clusters with similar performance. Second, the system performs a dynamic multi-criteria selection on resource requests by assigning scores to jobs according to their resource usage. Third, node groups and job scores are used to dynamically allocate jobs to the most appropriate resources in real time while keeping load balance in the heterogeneous cluster. The aims of this present paper are to optimize the resource utilization and achieve a high performance of the Yarn resource management by considering a heterogeneous environment. The experimental results demonstrate that the proposed algorithm provides the best utilization of available resources by 45% and 12% compared to fair and H-fair respectively, which implies a minimum job execution time in a heterogeneous cluster.

Individuals' flow's fluidifcation in the same way as the thinning of the population's concentration remains among major concerns within the context of the pandemic crisis situations. The recent COVID-19 pandemic crisis is a typical example of the aforementioned where on despite of the containment phases that radically isolate the population but are not applicable persistently, people have to adapt their behavior to new daily-life situations tempering Individuals' stream, avoiding tides, and watering down population's concentration. Crowd evacuation is one of the well-known research domains that can play a pertinent role to face the challenge of the COVID-19 pandemic. In fact, considering the population's concentration thinning within the slant of the "crowd evacuation" paradigm allows managing the flow of the population, and consequently, decreasing the probable number of infected cases. In other words, crowd evacuation modeling and simulation with the aim of better-exploiting individuals' flow allow the study and analysis of different possible outcomes for designing population's concentration thinning strategies. In this article, a new decision-making approach is proposed in order to cope with the aforesaid challenges, which relies on an independent Deep Q Network with an improved SIR model (IDQN-I-SIR). The machine-learning component (i.e., IDQN) is in charge of the agent's movements control and I-SIR (improved "susceptible-infected-recovered" individuals) model is responsible to control the virus spread. We demonstrate the effectiveness of IDQN-I-SIR through a case-study of individuals' flow's management with infected cases' avoidance in an emergency department (often overcrowded in context of a pandemic crisis).

Today vast and diverse event records of applications exist for almost every scientific domain, making their integration and intelligent exploitation challenging. Indeed, complex data require expressive data representation models. This work deals with the intention mining that is a very active and promising research area. Performance, flexibility, and adaptation are the biggest challenges for intention mining. Those issues have been illustrated from research on method engineering and guidance. Intention mining is the ability to predict a user’s goals. Knowing the user’s intention can support the decision-making of the network administrators. In addition, the user-friendliness is another big challenge to the intention mining in this paper. For this, based on the communication and coordination of intelligent Agents, a new multi-Agents based System approach is gathered in order to discover an intentional process model and provide specific recommendations. In addition, the input of intention mining is the trace of activities, which is an unstructured file because the activities are distributed from different sources. However, the dataset that will be used in intention mining must be well structured and filtered, so often some efforts are required to filter the relevant data. The main input of all algorithm used to discover intentional process model is the log file (traces activities), which is unstructured dataset and not ready to be feed as-is to machine learning algorithm. Therefore, this paper aims to describe the data preprocessing steps, which transform the unstructured log file to a structured one.

In this paper, we would like to assess the positive impact of the recommendation process during the professional activities of business actors. We are interested specifically in the improvement of the economic life of the handicraft women from emerging countries. To this end, we introduce a utility based recommender dealing with the procurement opportunities. Actually, we proposed a utility function which takes into account the weighted preferences and expectations of final users. The system is evaluated based on the gain to obtain if the proposed recommendations are adopted in addition to the satisfaction level of the final users.

The use of Unmanned Aerial Vehicles (UAVs) has evolved and increased recently both in civilian and military operations. In this research, we investigate the coverage of a given area using an autonomous UAV network and maintaining connectivity during the patrol; UAVs are equipped with an image and radio sensors, whose goal is to monitor a given area. Covering means that every position in the area should be covered at least by one UAV and connectivity consists to maintain the communication between UAVs and the base station during the patrol for better collaboration. Due to the communication range limit of UAVs, connectivity may then be needed to find inter-UAVs routing paths to satisfy the communication between UAVs and the base station.The problem is formulated and tested successfully, using the Solver CPLEX, as an integer linear programming model to solve it optimally. Computational experiments are generated on different grid sizes and multiple sensor ranges.

The complexity of real-world problems raises new challenges to evolutionary computation. Responding to those challenges, several methods are developed in particular the evolutionary algorithm. They are easy to implement and can provide good results. Among those methods, the Particle Swarm Optimization has some gaps in term of the convergence and diversity. In order to enhance its performance, we propose in this paper a population-distributed model using particle swarm optimization algorithm and island model structure. We investigate the effect of migration parameters such as the topology migration and the migration strategies. The empirical results using set of test functions show that the proposed PSO model is better to achieve an appropriate tradeoff between intensification and diversification.

The current industrial production environment is characterized by highly competitive markets, where customer requirements and expectations are becoming stronger in terms of quality, cost and time to delivery. The modern production systems are generally composed of machines that must participate in the manufacture of several types of products simultaneously and efficiently. The Flexible Job shop Scheduling Problem (FJSP) is one of the scheduling problems where each operation can be processed on different machine and its processing time depends on the used machine and this in a single factory. But in the recent years, many companies decide to move towards the decentralization of their factories which allows them to gain advantages towards their customers. So, we are now interested in Distributed and Flexible Job shop Scheduling Problem (DFJSP) where there is a set of geographically distributed factories in different locations. Each factory contains m machines on which n jobs must be processed. The Distributed scheduling problems and more specifically the DFJSP are much more complicated than standard problems because they involve not only the problem of assigning jobs to machines but also the problem of distribution of jobs in different factories. So, the DFJSP is harder than the FJSP. The DFJSP is classified, as most of scheduling problems, NP-Hard in complexity theory. In this paper, we propose a Chemical Reaction Optimization metaheuristic to solve the Distributed and Flexible Job shop Scheduling Problem in order to minimize the maximum completion time (makespan) among all factories. To evaluate the performance of our algorithm, a set of experiments are performed on well known benchmark instances in the literature.

We are concerned with supporting the air traffic controller in the management of the inbound traffic. We precisely focus on the careful modeling of the Aircraft Landing Problem (ALP) within an Air Traffic Control (ATC) system for an airport which may include a single or multiple runways. We propose a new detailed Mixed Integer Programming (MIP) formulation in which we break down the main problem into simultaneously three sub problems, the scheduling and sequencing of aircraft approaches and landings on the TMA resources (air segment(s) and runway(s)), their assignment and the control practice. Our achievement is the model solution using CPLEX and Eclipse. Computational results show the efficiency of the proposed approach.

A wide variety of Intelligent Transportation Systems (ITS) are developed to increase travel safety, improve traffic management and minimize environmental impact. C-ITS (cooperative ITS) are a subclass of ITS based on the communication between the vehicles and/or communication between the vehicles and the infrastructure. C-ITS are promising solutions to improve transport systems thanks to a local information processing. In that way, the information processing latency is minimal, the information is up to date and relevant to the current road environment context. Nevertheless the execution of these systems in the vehicular ad-hoc network (VANET) raises many difficulties. Our goal is to help the designer of a C-ITS to focus only on the expertise that is required to satisfy his objectives and not on the technical issues that the execution in a VANET implies. To reach this objective, we propose a platform which consists of a set of modules ITS (m-ITS), each of these m-ITS relies on a generic architecture hosting stationary and mobile agents dedicated to the support of C-ITS in VANET. The objective in deploying mobile agents is reinforcing the local processing of the information. Finally an example of C-ITS for contextual speed management illustrates our proposal.

The Constraint Satisfaction Problem (CSP) is acknowledged as a simple declarative formalism for modeling welldefined decision problems.However, real-world problems are usually ill-defined, especially, under uncertain circumstances.In such situation, uncertainty evokes the need for flexibility or softness where we accept satisfying some constraints to some degree.Moreover, when the relevance of some constraints depends on other factors, we should prioritize those constraints.Eventually, the modeled uncertainty, as well as the expressed soft and prioritized constraints induce preferences over the solutions set.Previous work employing mathematical uncertainty theories are either uncertainty-based frameworks or preference-based ones and the only attempt to handle both uncertainty and preferences is performed using two uncertainty theories under a commensurability assumption.In this paper, we propose a unifying CSP extension, labeled Belief CSP, that deals jointly with all these four concepts, i.e., uncertainty, soft and prioritized constraints and preferences over the solutions set, by exploiting the expressiveness of the belief function theory.

Job shop Scheduling Problem with Generic Time Lags is defined as a job shop problem with minimal and maximal delays between starting times of operations of different jobs. It belongs to a category of problems known as NP-hard. In this paper, a multi-agent model based on hybrid genetic algorithm is proposed for solving the Job shop Scheduling Problem with Generic Time Lags in order to minimize the makespan. The hybridization of genetic algorithm consists in using the greedy algorithm for the generation of initial population and using the tabu search metaheuristic in the mutation step. The proposed model is composed of two classes of agents: Interface Agent and a set of Scheduler Agents. Good performances of the proposed model are shown through different comparisons on a set of benchmarks from the literature based on 8 Carlier's instances and 40 Lawrence's instances.

Scheduling in production systems consists in assigning operations on a set of available resources in order to achieve defined objectives. The Flexible Job shop Scheduling Problem (FJSP) is one of the scheduling problems where each operation can be processed on different machine and its processing time depends on the used machine. But in the recent years, many companies decide to move towards the decentralization of their factories which allow it to gain advantages towards its customers. In the case of the classic Flexible Job shop Scheduling Problem, we assume that there is a single factory with a set of m machines and n jobs, but in Distributed and Flexible Job shop Scheduling Problem (DFJSP), there is a set of geographically distributed factories in different locations. Each factory contains m machines on which n jobs must be processed. The Distributed scheduling problems and more specifically the DFJSP are much more complicated than standard problems because they involve not only the problem of assigning jobs to machines but also the problem of distribution of jobs in different factories. So, the DFJSP is harder than the FJSP. The DFJSP is classified, as most of scheduling problems, NP-Hard in complexity theory. In this paper, we propose a decentralized model based on tabu search to solve the Distributed and Flexible Job shop Scheduling Problem in order to minimize the maximum completion time (makespan). To evaluate the performance of our model, a set of experiments are performed on benchmark instances well known in the literature.

In technology-enhanced learning, semantic annotations have been employed to attach semantic metadata to learning materials in order to significantly enhance their accessibility by human users and machines as well. In this paper, we present an ontology-based multi-level semantic representation model that aims to enrich the description of learning objects with semantics regarding their subjects, competencies and instructional roles. More specifically, the proposed model uses three ontologies: a subject domain ontology describing the domain concepts and the relations that are covered by the subject matter being taught, a competency ontology describing the competency-related characteristics of learners and learning resources, and an instructional role ontology specifying the instructional role(s) a learning object can play in an instructional setting. To demonstrate the feasibility of our model, an illustrative example is given that explains how learning object semantics can be represented with different granularities.

Software refactoring aims at optimizing software modularization by improving internal software structure without altering its external behavior. There exists various approaches for suggesting refactoring opportunities, based on different sources of information, e.g., structural, semantic, and historical. In this paper, we propose a data fusion model to combine different sources of information in order to identify refactoring opportunities and we instantiate it to support Move Class refactoring. We report the results of our validation conducted on four software systems and we show that our proposal improves the modularization quality by 29% and that our tool is able to provide meaningful recommendations for move class refactoring. Specifically, more than 70% of the recommendations were considered meaningful from the developers' point of view.

Particle Swarm Optimization (PSO) algorithm is a metaheuristic that has been used for solving optimization problems. In this method, many modifications have been carried out in order to improve the search performance. Furthermore, island models is a structured population mechanism used to preserve the diversity and thus to improve the population performance. The aim of this paper is to integrate dynamic island models with PSO algorithm to improve its convergence and its diversity properties where the new method is referred to as island PSO. The dynamic regulation of migrations aims to distribute the particles in the search space. The experimental results, using a set of benchmark functions show that the island model context is crucial to the PSO performance and the comparative study shows the efficiency of the integration of dynamic island models.

The Distributed Permutation Flowshop Scheduling Problem (DPFSP) is one of the most computationally complex problems. It has gained a wide attention not only in theoretical studies but also in manufacturing industry. In recent years, a lot of work has been done and many heuristics and metaheuristics have been proposed to tackle the DPFSP. Unfortunately, all the existing algorithms are centralized despite the fact that the distributed approaches are known to be more practical for the complex scheduling problems ones. Thus, we argue that distributed artificial intelligence techniques, namely Multi-Agent Systems (MAS), offer an appropriate tool to tackle problems of a distributed nature when they are properly designed and implemented. Thanks to their flexibility, adaptively and extensibility; MAS represents a promising variant to achieve a better performance. In this study, by combining the population-based evolutionary searching abilities of Chemical Reaction Optimization (CRO) metaheuristic with the capabilities of MAS in modeling hard combinatorial problems, we suggest an agent-based evolutionary algorithm called CROMAS to effectively solve the DPFSP. We tested our algorithm on well-known benchmark instances and compared its performance with respect to other recent methods. Experiments reveal that CROMAS is very effective and able to provide competitive results.

The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories geographically distributed and with determining a good operation schedule of each factory. The objective is to minimize the global makespan over all the factories. This paper is a first step to deal with the DJSP using three versions of a bio-inspired algorithm, namely the Ant Colony Optimization (ACO) which are the Ant System (AS), the Ant Colony System (ACS) and a Modified Ant Colony Optimization (MACO) aiming to explore more search space and thus guarantee better resolution of the problem. Comprehensive experiments are conducted to evaluate the performance of the three algorithms and the results show that the MACO is effective for the problem and AS and ACS algorithms in resolving the DJSP.

Scheduling in production systems consists in assigning operations on a set of available resources in order to achieve defined objectives. The Flexible Job shop Scheduling Problem (FJSP) is one of the scheduling problems and also an extension of classical Job shop Scheduling Problem (JSP) such that each operation can be processed on different machine and its processing time depends on the used machine. The FJSP is classified, as most of scheduling problems, NP-Hard in complexity theory and can be decomposed into two sub-problems: a routing sub-problem, which consists of assigning each operation to a machine out of a set of alternative machines, and a scheduling sub-problem, which consists of sequencing the assigned operations on all selected machines in order to attain a feasible schedule with optimized objectives. In this paper, we propose a decentralized model named Multi Agent model based on Chemical Reaction Optimization with Greedy algorithm (MACROG-FJSP) to solve the FJSP in order to minimize the maximum completion time (Makespan). Experiments are performed on well known benchmark instances proposed in the literature which are Fattahi, Kacem, Brandimarte and Hurink instances to evaluate the performance of our model.

Over the last century, different learning theories have shaped the world of education and training before shifting to the competency-based approach (CBA). This new paradigm to teaching and learning aims to ensure that every student has to graduate with the competitive competencies of lifelong learners and is ready to enter the workforce and begin functioning in entry-level positions. However, despite the growing interest in competency-based learning and training, this field still faces numerous challenges, essentially the lack of consensus about an interoperable description of competency evidences. Indeed, the move towards CBA has created a need for effective instruments that support and assess competency development. In this context, the electronic portfolio (e-Portfolio) emerged as a suitable tool that helps learners collect and manage multiple kinds of assessment evidences linked to the program’s competencies from multiple sources. In this paper, we propose an ontology-based approach to e-portfolio modeling which relies on Semantic Web technologies to formally and semantically describe portfolio artifacts that evidence the achievement of one or several competencies. The proposed ontology is structured according to official e-Portfolio specifications, namely IMS ePortfolio and JISC Leap2A. In addition, other existing approaches to e-Portfolio modeling reported in the literature have been explored to avoid misinterpretation of these specifications. Furthermore, a comparative study of common e-Portfolio systems has been carried out in order to gain a fairly accurate idea of the generic structure of an e-Portfolio.

In this paper, we propose a Multi-Agent based Hyper-Heuristic algorithm for the Winner Determination Problem named MA H 2 -WDP. This algorithm explores a set of cooperating agents to select the appropriate operation using learning techniques. MA H 2 -WDP is specialized for local search methods and evolutionary methods where the following agents are seeking to improve the search within reinforcement learning: the mediator agent, two local search agents, the perturbation agent and two recombination agents. Our computational study shows that the proposed algorithm performs well on the tested benchmark instances in terms of solution quality.

Since the emergence of the "Airport Congestion" that serves over four million passengers per year, the latter crucial component of the air transportation system is becoming a major bottleneck in air traffic control operations. In spite of the pressure of this situation, many operations within an airport are still carried out by man-made systems. As an example, the air traffic controllers are still responsible for implementing arrival sequencing and scheduling solution which made the aircraft landing operation in the terminal maneuvering area a complex and difficult problem for the air traffic control. In this context, this research presents an exact method for the static case of the aircraft landing problem to a pair of runways. The computational experiments, tested with data from Tunis Carthage Airport, show the benefits of the proposed approach compared to the air traffic control rule applied within the relevant airport.

This paper presents a new multi-objective technique which consists of a hybrid between a particle swarm optimization approach (PSO) and tabu search (TS) technique. The main idea of the approach is to combine the high convergence rate of PSO with a local search technique based on Tabu Search. Besides, in our study, we proposed to apply local search to improve the capacity of exploitation of PSO. The mechanisms proposed are validated using fifteen different functions from specialized literature of multi-objective optimization. The obtained results show that using this kind of hybridization is justified as it is able to improve the quality of the solutions in the majority of cases.

Solving scheduling problems is an important branch of operational research field. It consists in allocation number of jobs to machines taking into consideration a set of constraints. Recently, a new generalization of the permutation flow shop scheduling problem with multi-factory environment has been proposed. In this work, we suggest to apply a Chemical Reaction Optimization (CRO) metaheuristic to solve this problem in order to minimize makespan or the total duration of the schedule. An experimental study is carried out in order to analyze the performance of the proposed algorithm and to compare it with powerful algorithms on standard benchmarks.

In systems based robotic cells, the control of some elements such as transport robot has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and a Single Robot (JSPT-SR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by a single transport robot. Hence, the JSPT-SR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-SR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using benchmark data instances from the literature of JSPT-SR. New upper bounds are found, showing the effectiveness of the presented approach.

Services providers and requestors, in ubiquitous environments, are characterized by the heterogeneity of their devices and the constant changing of the network bandwidth. These characteristics pose a big challenge to service selection methods. This paper describes a new service selection method based on Multiple Linear Regression (MLR) techniques. In order to ensure the selection of the best service in terms of provider's quality of service (QoS), an MLR model is defined estimating especially Response time, returned from service provider. Response time is mainly influenced by three independent variables: bandwidth, processor speed and memory. We validate our model by testing five different types of devices (provider side) in five different types of network (i.e. different values for bandwidth). The results show that 97.24% of the variation in the response time can be explained by our model.

The literature on web service (WS) discovery recognizes a major problem in the requirements of service consumption. Current works on WS discovery focuses on WS discovery scenario and neglect the analysis of the intervention of service consumer. Therefore, the problem of service consumer needs must be solved to guarantee the pertinence of WS selection and the consumer satisfaction. In this paper, we defined a comparative study between the WS discovery approaches. Based on the comparison framework, analyzes lead us to propose a specific approach to SOA named B-WSD approach based on behavioral aspect. The behavioral description of a WS consists in describing the order of invocation of WS operations more precisely the execution manner of composed WS.

A cloud provider provides, on-demand, physical computing resources to clients. It enables clients to improve their computing delivered resources in virtual machines. However, the cloud provider faces major problems impacting its correct operation. One of these problems is that a cloud provider cannot deliver more resources when it has not enough resources during peak hours. Another problem related to providers is their inability to satisfy all client's requirements. In order to increase the reliability and the availability of cloud providers resources, we have proposed a distributed approach based on Contract Net Protocol (CNP) to overcome the limited resources problem. We have proposed also an extension for Open Virtualization Format (OVF) standard to describe resources provisioning in cloud federation. This extension includes more information about the client, the provider identification and the quality of service. To prove the efficiency and the effectiveness of our approach, we have suggested a real case study illustrating how a client can interact with our approach for the provision of two-tier web application. In addition to that, we have implemented a prototype to evaluate the key idea presented in this paper through a set of experiments.

In this paper, we try to improve health care services by treating the variation among several issues related to the health field, particularly the Home Health Care Problem (HHCP). In fact, Home Health Care Service (HHCS) is known as a care mode allowing patients who suffer from complex and evolving diseases to benefit at home from medical and paramedical coordinated care that can be only provided in hospitals. In this work, we treat the Caregivers' Tours Problem (CTP) and conflict management sanitary visits to patients' homes. We developed a new three-phase metaheuristic, which optimizes both the daily caregivers' tours to minimize the travel costs and maximizes the planned services in order to address potential conflicts. The obtained numerical results, compared with those provided by the other existing approaches, are motivating and encouraging.

The development of new digital technologies is swiftly rising. Thus, the cloud computing is grabbing-attention of information technology communities. In this context, diverse security issues are amplified. Particularly, access control seems of main importance because it ensures diverse security services, such as, authentication, identification, confidentiality and integrity. Several works are devoted for designing access control models. In this paper, we are particularly interested on distributed access control approaches. According to identified drawbacks of Multi-OrBAC model, we introduce a new distributed access control model for cloud computing based on Mobile Agent.

Support Vector Machines (SVM) is a statistical classification approach which has been successfully applied to various types of problems. However, it has remained largely unexplored for Arabic recognition. SVMs are originally designed for binary classification problems. For multi-class problems, several methods used a combination of binary SVMs while some others solved the problem in one step. This paper introduces an evaluation of five SVM methods for the Arabic characters recognition problem; three are based on binary classifiers, and two considers all classes at once. The selected algorithms are compared in terms of training time, testing time and accuracy. Experiments conducted using the Arabic Printed Text Image Database-Multi-Font(APTID/MF ) showed that the “one-against-one method” is the robust, fast and produces a very good score rate at a reasonable time.

The concept of Cloud computing is to outsource IT data in remote servers. Data security issues are still being discussed, but the economic and ergonomic gains are not disputed by anyone. Cloud computing is also an opportunity for universities that wish to use information technologies and communication. Higher education institutions must exploit the opportunities offered by Cloud computing while minimizing security risks associated to allow access to advanced IT infrastructure, data centers, applications and sensitive information. In this work, we propose a survey of Cloud computing in Tunisian universities. The result of our research shows that Cloud computing can be used in higher education institutions because it offers an efficient and strategic use of technology to reduce the costs of implementation and maintenance. The paper takes into account the best practices for the use of Cloud computing in universities and higher education. Moreover, we propose a migration strategy, model and architecture for understanding how universities can move from the use of local servers to the use of application as a service.

In Multi-Agent Systems, first-class interaction protocols are the ones which implementations are decoupled from the agents. This paper introduces and establishes the contribution of RPI.Idiom which is a high-level language for specifying such protocols. RPI.Idiom interaction protocols satisfy several properties. They are role-based which means that they are abstract in regard of the agents. They can be automatically enacted and executed by agents in total autonomy. They are structurally generic, i.e. they are not specified as exchanged messages but in terms of behaviours which cover message exchange. They are meaningful, i.e. they explicit their own meanings. They are socially generic i.e. the meanings could express diverse forms of social prescriptions (e.g. commitments). Finally, they are modular which implies that they are reusable and potentially composable.

Every problem that can be described by a set of variables and a set of constraints among those variables can easily be cast as a Constraint Satisfaction Problem (CSP). In spite of its simplicity, the standard CSP has proven unsuited for modeling ill-defined decision problems, especially, under uncertain circumstances. In such situation, uncertainty evokes the need for softness. Moreover, when the relevance of some constraints depends on other factors, we should prioritize those constraints. Eventually, the modeled uncertainty, as well as the expressed soft and prioritized constraints induce preferences over the solutions set. Previous work employing mathematical uncertainty theories are either uncertainty-based frameworks or preference-based ones. In this paper, we propose a unifying CSP extension, based on the Dempster-Shafer theory, which deals jointly with all these four concepts, i.e., uncertainty, soft and prioritized constraints and preferences over the solutions.

Recommending personalized events from the huge amount of information on the social web is a challenging problem. Besides, dealing with such recommendation improves significantly the professional communities activities. Likewise, it helps them to make appropriate decisions while saving time and efforts. In this paper, we propose a hybrid recommender system which suggests suitable events to the HanDicraft Women (HDWs) from Tunisia and Algeria. The established system considers the final user needs and demographic attributes. Indeed, it combines the knowledge-based and demographic approaches together. Useful information related to the HDWs and available events are represented through a semantic formalization: Friend Of A Friend (FOAF) Ontoloy and Online presence Ontology (OPO). Moreover, the recommender is able to manage the dynamicity and heterogeneity of the HDWs environments since it is based on a multi-agent architecture.

This paper presents a research survey on the Home Health Care. It is a way of caring for patients who suffer from complex, non-serious diseases and especially evolutionary ones. At their homes, they benefit from medical and paramedical cares that can be only provided by hospitals. This form of treatment has a positive impact on both the patients and the state. To study its evolution as well as its development and to understand the difference in this new mode of care, we have devoted the first part of this investigation for the home health care history. In the second part, based on official data published on the websites of the European and American States, we have analyzed home health care centers, the most recognized ones at the national and internal levels, to distinguish similarities and differences between them and especially to determine their specifications and limitations. To overcome the latter, it is necessary to dig new leads of research; that is why the founders of these institutions have resorted to scientists. The third section covers the state of the art since the 70s. In this part, we have depicted the research work that contributed to resolve home health care problems.

The multimodal transport is a solution adopted by the governments to solve many challenges like the energy consumption and the pollution. Actually, the multimodal transport faces many problems as those related to the distribution, the focus of many researchers who have classified it as a NP-hard problem. The goal of this work is to develop a distributed guided genetic algorithm to solve the problem of multimodal transport, specially the disturbance. The solution must be valid in the normal case and in the degraded mode. So, this study aims to improve the quality of services offered to users. In fact, our approach is based on evolutionary algorithms, and more precisely on the genetic algorithm. We use hybridization in the selection operator and integration of a new structure in the mutation operator which supports on a multi-criteria method for the detection of itineraries.

The Flexible Job Shop scheduling Problem (FJSP) is an extension of the classical Job Shop scheduling Problem (JSP) that allows to process operations on one machine out of a set of alternative machines. It is an NP-hard problem consisting of two sub-problems which are the assignment and the scheduling problems. This paper proposes a hybridization of two metaheuristics within a holonic multiagent model for the FJSP. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a cluster agents set uses a local search technique to guide the research in promising regions. Numerical tests are made to evaluate our approach, based on two sets of benchmark instances from the literature of the FJSP, which are the Brandimarte and Hurink data. The experimental results show the efficiency of our approach in comparison to other approaches.

Security is a very important concept in the growth and the development of the mobile agent technology. However, in most of researches, security properties are envisaged in the implementation phase. Nevertheless, the integration of security properties in all phases of software development can provide more secure mobile agents based systems. In this paper we are interested to model the security properties in order to protect stationary agents during their communications with visitor mobile agents.

The workflow satisfiability problem has been studied by researchers in the security community using various approaches. The goal is to ensure that the user/role is authorised to execute the current task and that this permission doesn't prevent the remaining tasks in the workflow instance to be achieved. A valid authorisation plan consists in affecting authorised roles and users to workflow tasks in such a way that all the authorisation constraints are satisfied. Previous works are interested in workflow satisfiability problem by considering intra-instance constraints, i.e. constraints which are applied to a single instance. However, inter-instance constraints which are specified over multiple workflow instances are also paramount to mitigate the security frauds. In this paper, we present how ECA (Event-Condition-Action) paradigm and agent technology can be exploited to control authorisation plan in order to meet dynamic constraints, namely intra-instance and inter-instance constraints. We present a specification of a set of ECA rules that aim to achieve this goal. A prototype implementation of our proposed approach is also provided in this paper.

Many problems in multi-agent systems can be described as Distributed Constraint Satisfaction Problems (DCSPs), where the goal is to find a set of assignments to variables that satisfies all constraints among agents. However, when reallife application problems are formalized as DCSPs, they are often over-constrained and have no solution that satisfies all constraints. Moreover, the globalization of the economy and democratization of the Internet, boosted by the huge growth in information and communication technologies, have largely contributed to the expansion of numerous distributed architectures. Thus this paper provides a new distributed management and decision support system suitable to these interdependencies and these complex environments. We present a Distributed Optimization under Constraints Basic Relax (DOCBRelax) as a new framework for dealing with over-constrained situations. We also present a version of this framework called DOC-MaxRelax and a new algorithm for solving Distributed Maximal Constraint Satisfaction Problems (DMCSPs).

An effective discovery system must be able to retrieve services responding to the users’ specific preferences in a changing and dynamic environment. Actually, the existing discovery systems have three problems. Firstly, some of them fail to find Web services providing the same QoS as defined in their related description files, since the QoS data are considered as static. Secondly, the discovery systems based on negotiation lack the accuracy in simulating similarly the real humans’ interactions. Thirdly, the negotiation based approaches implemented to discover services are static and don’t consider contexts as well as characteristics of each provider. These shortcomings affect negatively the systems performance and usability. Consequently, the quality of the returned services as well as the systems’ reputation will be deteriorated. In this paper, we propose an hybrid discovery approach based on negotiation that solve these drawbacks. We argue that our approach enhances the discovery system performance and usability by implementing a negotiation process that is closer to humans’ interactions. Moreover, by considering the existing dependencies between the concurrent negotiations, the discovery process will be more efficient. Unlike previous work, the negotiation process is dynamic by taking into account the provider’s context and reputation.

Vehicular ad hoc network is one of the most promising applications of MANET. However, they have special properties such as high mobility, network portioning and constrained topology which require smaller latency and higher reliability. These vehicles that move along the same road are able to communicate either directly to the destination or by using the intermediate node, such as router. Therefore, designing an efficient routing protocol for all VANETs scenarios is very hard. A lot of researches about routing in VANETs are considering DSDV routing protocol as the most suitable protocol for mobility environment. But DSDV generates a large volume of control packets and takes up a large part of available bandwidth. In this paper, we propose an improving DSDV routing protocol based on multi-agent system approach to solve the performance problems mentioned above. Experimental results show promising results regarding the adoption of the proposed approach.

Suitable suppliers’ recommendation forms the basis for a successful procurement process. An automated personalization of procurement opportunities relies on the buyer and the seller profiles consistency. However, dealing with their profiles matching is not a trivial task. In fact, each contextual detail may play a crucial role in the decision making procedure especially when final users express very specific and variable needs in a heterogeneous and inconstant environment. In this paper, we designed and developed a multi agent system (MAS) for the supply chain automatization. It includes two supplier selection levels followed by a negotiation module relying on the handicraft woman online situation. Several handicraft business ontologies in addition to multi-side profile ontology are developed in order to drive the agent communication with the internal and external environment.

The Learning Management System (LMS) Moodle is currently the most popular software solution which provides many modules for various teaching and learning purposes. However, several aspects relevant for competency management are typically missing in Moodle. This paper proposes an ontology-based competency management application which is developed as a Moodle extension for supporting the development and assessment of competencies inside a course. Details about the competency ontology adopted for designing the competency-based course structure as well as the competency management features embedded into Moodle are presented. By incorporating these features into a LMS, it becomes possible to manage target competencies together with their associated evidence items and assess proficiency levels reached by students for each target competency. In addition, it becomes possible to generate different types of competency reports depending on the target role (teachers, students or administrators).

This paper presents a mathematical model that aims at describing and to resolving the Stochastic DRP with an approach based on the NSGAII. DRP consists in taking the passenger from a place of departure to a place of arrival. The ultimate aim is to offer an alternative to individually and collectively optimized displacements. The DRP is classified as NP-hard problem. That's why, most researches have been concentrated on the use of approximate methods to solve it. Indeed, the DRP is a multi-criteria problem. The proposed solution aims to reduce both route and duration in response to a certain quality of service provided. Actually, during the system of transport on demand (TOD) there are many problems that can inhibit the proper functioning of the system, such as failure of the car and bottling. In this work, we have contributed to the resolution of stochastic DRP in solving possible problems when touring vehicles such as bottling, vehicle failures, accidents and vehicle occupancy service. We have developed an approach which is based on the genetic algorithm kind of the kind NSGAII to optimize travel time and elapsed travel time during the shot, taking into account the possible rate of risk in this selected itinerary.

This paper proposes an approach which is based on a multi objective genetic algorithm to resolve the vehicles routing problem with time windows (VRPTW). The context of this problem is to plan a set of routes to serve heterogeneous demands respecting several constraints (only one depot, vehicles limited capacity, windows of time). We used an approach based on a multi-objective optimization to resolve this problem. The criteria to be optimized are the number of used vehicles and the total required distance. We propose a method of resolution which is based on a hybridization of a genetic algorithm NSGAII (Not dominated Sorting Genetic Algorithm II) and the BCRC algorithm (Best Cost Route Crossover).

In this paper, we are interested in the modeling and the resolution of the dynamic and discrete berth allocation problem which is noted DDBAP. To resolve this problem, we propose a heuristic approach of optimization which combines two concepts: agent and heuristics. This approach is based on the use of the multi-agent negotiation, the contract net protocol, and a set of heuristics such as the WorstFit arrangement technique and the LPT policy. The objective of our work is then, to solve the problem of scheduling n tasks on m parallel identical machines. The criterion that we aim to minimize is the makespan (in analogy with the P∥Cmax problem) having a set of constraints to be satisfied. We developed our model of negotiation using the Jade platform. We finish this work by presenting various simulations to show the performance of the proposed heuristic and the contribution of our approach compared to other already existing approaches.

Explanation of intelligent systems was and is still an important issue in Artificial Intelligence discipline, especially, in com-plex systems like Multi-Agent Environments. In fact, during its uncontrollable execution, the agent reasoning is not clearly reproducible for the user. The complex nature of such systems requires methods and tools to make them intelligible. In this context, we propose to provide users with traceability, more execution transparency, and to give them the possibility to become familiar with such dynamic and complex systems and to understand how solutions are given, how the resolution has been going on, how and when interactions have been performed. For this purpose, we develop an intelligent approach based on three modules, namely, the observation module, the modeling module, and the interpretation module. The first one generates the explanatory knowledge. The second one represents this knowledge in extended causal maps formalism. The third one analyzes and interprets the built causal maps using a first order logic to produce reasoning explanations.

In this paper we present a specification of the most common static and dynamic workflow authorisation constraints. We propose an authorisation model that includes a planning phase, an execution phase and an adjustment phase. In addition, we focus on how the problems of role-task assignment and user-task assignment are respectively translated into CSP (Constraint Satisfaction Problem) and DyCSP (Dynamic constraint Satisfaction Problem) and solved using the explanation concept. In case of an inconsistent assignment problem, we propose to restore problem consistency based upon inconsistency explanation.

Role-Based Access Control (RBAC) model has been developed as an alternative to traditional approaches to handle access control in workflow systems. Accordingly, authorisation constraints must be defined to enforce the legal assignment of access privileges to roles and roles to users. The authorisation planning ensures that there is at least one way to complete the workflow instance without breaching any of the authorisation constraints. Authorisation planning with considering intra-instance constraints has been discussed in the research literature. However, the inter-instance constraints also need to be considered to mitigate the security fraud. In this paper, a novel authorisation system that incorporates intra-instance and inter-instance constraints is proposed. It includes the planning phase, the execution phase, and the adjustment phase. It is in charge of generating user/role assignment plans, verifying them and eventually updating them to take into account the dynamic (intra-instance and inter-instance) constraints. Besides, grounded upon agent technology and publish-subscribe communication model, a mechanism for the consideration of dynamic constraints (intra-instance and inter-intance) to generate valid assignment plans is demonstrated.

The problem of assignment containers to AIVs in a container terminal is a complex problem, it is a combination of several problems, the dispatching problem, the vehicles routing problem and the scheduling problem. Each problem of these depends on some criteria, this makes the global problem as a multi-criteria problem. To solve this type of problem, the idea is to consider it like a mono-objective problem depending on each criterion separately. An aggregation function is calculated according to different criteria. The coefficient value of each criterion is proposed by an expert, these values can give the nearest Pareto front solution. The approach proposed in this work is a hybrid approach, genetic algorithm and Dijkstra algorithm. This approach is tested with different numbers of vehicles, for each problem separately, in order to have the best solution. The numeric results show the performance of our approach for the multi-objective problem.

For some years, competency-based learning and training has known a growing interest, especially in conjunction with proliferation of the terms "knowledge society", "citizen mobility", or "globalization". Competency modeling becomes an important concept in many domains, especially in human resource development and e-Learning. In this paper, we present a review of relevant competency metadata standards and some ontology-based approaches for competency modeling.

The number of Web services has substantially increased in response to the needs of business activities. Moreover, toolsets and APIs ensuring the easy development and deployment of these services have emerged. As a result, the number of registries holding the great deal of Web services has also increased. Therefore, binding these services, whenever the user needs to reuse them, is time and effort consuming. To cope with this problem and in response to the limits of current approaches, we propose a local repository-based approach to optimize the binding of the frequently used Web services. Furthermore, an experimental study is presented to situate the proposed approach to other ones dealing with the binding feature of Web services.

Recent studies on Evolutionary Multi-objective Optimization (EMO) aim at focusing the search only on those portions of the front which satisfy the preferences of the Decision Maker (DM), i.e., the Regions Of Interest (ROIs), rather than approximating the whole Pareto front. Most studies assume the uniqueness of the DM which is not the case for several decision making situations. In this study, we address this problematic by providing the DMs with an agent-based negotiation support system to aggregate their conflicting preferences before the beginning of the evolutionary process. This negotiation system helps the DMs to confront and adjust their preferences through a number of negotiation rounds. The system output is a set of social preferences which will be injected subsequently in a preference-based EMO Algorithm (EMOA) in order to guide the search towards a satisfying social ROI. The usefulness of the proposed system is demonstrated through a case study.

Many companies aim to use Web services to integrate heterogeneous or remote applications in SOA (Service Oriented Architecture) contexts. Indeed, one of the main assets of service-orientation is a composition to develop higher level services, so-called composite services, by re-using existing services. Since many available Web services provide overlapping or identical functionality, with different Quality of Service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. However, for a composition, we can have different combinations and execution paths. Particularly, a composite service can generate different schemes that give various QoS scores.

As more and more functionally similar Web Services from providers with different Quality of Service (QoS) are available on the Web, a selection needs to be made to determine which services are to participate in a given composite service. Moreover, QoS becomes one of the most important factors for Web Service selection. Indeed, one of the main assets of service-orientation is composition to develop higher level services, so-called composite services, by re-using existing services. However, in distributed environments, the use of services without any quality guarantees from the service providers can negatively affect a composite service. Particularly, a system composed of services that ensures the QoS of individual services which should achieve optimal Composite QoS (CQoS) is always a problem during the search. Meanwhile, accuracy and speed of Web Service selection come to be the new barriers. This paper deals with the selection of composite Web services on the base of Multi-Agents negotiation. The objective of these agents is to find out the best CQoS. The proposed Multi-Agents architecture is compared to an existing approach in terms of execution time. The experiments have demonstrated that our approach takes a largely lower time.

The need of interoperable e-government services is addressed through the use of web services where sensitive services need to be granted to only authorized subjects from different organizations. In this paper, we propose a Trust and Dynamic Role Based Access Control model (TDRBAC) which deals with the specific requirements of e-government services. It effectively enhances the access control level since it is based on the trust level notion. The trust level evaluation is based on contextual attributes to assign to user role the appropriate view during the active session. The TDRBAC model is sensitive to the internal or external arisen events and it incorporates them in the access decision which makes it suitable for e-government dynamic environment.

Nadir point represents important information to multi-objective optimization practitioners. Along with the ideal point, the nadir point: (1) provides information about the ranges of the objectives at the Pareto optimality stage, (2) helps the decision maker to easily state his/her preferences, (3) facilitates the visualization of Pareto optimal solutions for highly dimension multi-objective problems, etc. Contrary to the ideal point which can be easily computed by optimizing each objective individually over the search space, the nadir point is constructed from worst objective function values of Pareto optimal solutions which makes the accurate estimation of the nadir objective values a difficult task especially when the number of objective functions increases. In this paper, we propose a new memetic preference-based multi-objective evolutionary algorithm, termed MR-NSGA-IIN, to estimate the nadir point. The basic idea is to use extreme solutions from the best non-dominated front as mobile reference points. The mobile reference points are updated in every generation by means of a gradient-based local search procedure in order to speed up the convergence towards the Pareto optimal extreme solutions. The performance assessment of MR-NSGA-IINis carried out on a set of three-to twenty-objective unconstrained/constrained linear/non-linear problems. The proposed approach has shown competitive and better results when compared to other recently proposed nadir point estimation approaches.

One of the current challenges in the development of robot control systems is making them capable of intelligent and suitable responses to changing environments. But, the control of the robot’s behavior in uncertain and dynamic environments is very challenging when the problem is how to guarantee the robot’s safety by minimizing the interaction with other actors. The most popular methods are based on reactive local navigation schemes that tightly couple the robot actions to the sensor information. These approaches are well based on distance between the robot and the obstacles. This information does not allow the robot to make an intelligent decision while it navigates in unknown environments. Whereas, the robot needs to anticipate environment evolution in order to minimize interaction and avoid conflict with other agents. In this paper, we present a multi-agent simulation model of an autonomous robot in dynamic and uncertain environments. We focus on cases of interactions between agents sharing the same space. The robot should minimize the conflict with other agents when it navigates headed for its goal. Our model is based on fuzzy logic technique in order to deal with the uncertainty of perception.

Autonomic computing is about systems that can manage themselves. Self-management includes self-configuration, self-healing, self-optimization, etc. (self-* properties). Agent technology offers key advantages for the development of autonomic computing systems as it supports autonomy, adaptability, etc. Current Web service standards and technologies don't provide a suitable architecture in which all aspects of self-management can be designed. In this paper, we present an agent-based framework for autonomic Web services. This framework is based on a multi-agent system made up with five agents namely a Planning agent, an Execution agent, a Composition agent, a Discovery agent, and a Monitoring agent.

Intelligent software based on agent technology emerges to improve system design, and to increase enterprise competitive position as well. The competitiveness is based on the cooperation. Thus, within this cooperation, conflicts may arise. Argumentation theory has become an important topic in the field of Multi-Agent Systems and especially in the negotiation problem. Moreover, research on trust and reputation is a recent discipline oriented to increase the reliability and performance of electronic communities. In this paper, first, an overview of a proposed model MAIS-E2(Multi Agent Information System for an Extended Enterprise) and a proposed argumentation based negotiation framework: Relationship-Role and Interest Based Negotiation (R2-IBN) framework is presented. Then, we focused mainly on the description of the proposed trust and reputation model. For that, we conduct an hybridization of approaches: fuzzy, mathematical and statistical.

Evolutionary multiobjective optimization (EMO) methodologies have gained popularity in finding a representative set of Pareto optimal solutions in the past decade and beyond. Several techniques have been proposed in the specialized literature to ensure good convergence and diversity of the obtained solutions. However, in real world applications, the decision maker is not interested in the overall Pareto optimal front since the final decision is a unique solution. Recently, there has been an increased emphasis in addressing the decision-making task in searching for the most preferred alternatives. In this paper, we introduce a new variant of the Pareto dominance relation, called r-dominance, which has the ability to create a strict partial order among Pareto-equivalent solutions. This fact makes such a relation able to guide the search toward the interesting parts of the Pareto optimal region based on the decision maker's preferences expressed as a set of aspiration levels. After integrating the new dominance relation in the NSGA-II methodology, the efficacy and the usefulness of the modified procedure are assessed through two to ten-objective test problems a priori and interactively. Moreover, the proposed approach provides competitive and better results when compared to other recently proposed preference-based EMO approaches.

Current best practices for identifying malicious activity in a network are to deploy network intrusion detection systems. Anomaly detection approaches hold out more promise, as they can detect new types of intrusions because these new intrusions, by assumption, will deviate from ldquonormalrdquo behavior. But these methods generally suffer from several major drawbacks: computing the anomaly model itself is a time-consuming and processor-heavy task. To avoid these limits, we propose a mobile agent based model for intrusion detection system, called MAFIDS, including new metrics issued from emergent indicators of the agent synergy and a proposed event correlation engine. We detail the implementation of our model showing its capabilities to detect the SYN Flooding attack in a short time and lower false alarm rate by comparing it to SNORT.

Lateral Transshipments afford a valuable mechanism for compensating unmet demands only with on-hand inventory. In this paper we investigate the case where locations have a limited storage capacity. The problem is to determine how much to replenish each period to minimize the expected global cost while satisfying storage capacity constraints. We propose a Real-Coded Genetic Algorithm (RCGA) with a new crossover operator to approximate the optimal solution. We analyze the impact of different structures of storage capacities on the system behaviour. We find that Transshipments are able to correct the discrepancies between the constrained and the unconstrained locations while ensuring low costs and system-wide inventories. Our genetic algorithm proves its ability to solve instances of the problem with high accuracy.

We introduce in this paper a new multi-objective memetic algorithm. This algorithm is a result of hybridization of the NSGA-II algorithm with a new designed local search procedure that we named Pareto Hill Climbing. Verification of our novel algorithm is carried out by testing it on two sets of multi-objective test problems and comparing it to other multi-objective evolutionary algorithms (MOEAs) and other multi-criterion memetic algorithms (MMAs). Simulation results show the algorithm ability in tackling continuous multi-objective problems in terms of convergence and diversity. Our hybrid algorithm (1) outperforms pure MOEAs, (2) is competent with other gradient based MMAs, and (3) can solve non differentiable problems.

The data warehouse (DWH) is usually presented as a centralized database. In this paper, we propose a new approach to manage data storage and distribution in a data warehouse environment. This approach deals with the dynamic data distribution of the DWH on a set of servers. The data distribution that we consider is different from the "classical" one which depends on the data use. The distribution in our approach consists in distributing data when the server reaches its storage capacity limit. This distribution assures the scalability and exploits the storage and processing resources available in the organization using the data warehouse. It is worth noting that our approach is based on a multi-agent model mixed with the scalability distribution proposed by the Scalable Distributed Data Structures. The proposed multi-agent model is composed of stationary agent classes: Client, Dispatcher, Domain and Server, and a mobile agent class called Messenger. These agents collaborate and interact to achieve automatically the storage, the splitting (distribution), the redirection and the access operations on the distributed data warehouse. In this paper, we demonstrate the improvements obtained when we have used the multi-agent system and the Messenger agents in the data storage operation.

We propose in this paper a generic algorithm based on ant colony optimization to solve multi-objective optimization problems. The proposed algorithm is parameterized by the number of ant colonies and the number of pheromone trails. We compare different variants of this algorithm on the multi-objective knapsack problem. We compare also the obtained results with other evolutionary algorithms from the literature.

D3G2A is a new multi-agent approach which addresses additive constraint satisfaction problems (SigmaCSPs). This approach is inspired by the guided genetic algorithm (GGA) and by the dynamic distributed double guided genetic algorithm for Max_CSPs. It consists of agents dynamically created and cooperating in order to solve the problem. Each agent performs its own GA. First, our approach will be enhanced by a new parameter called guidance operator. The latter allows not only diversification but also an escaping from local optima. In the second step, the agents performed GAs will, no longer be the same. This is stirred by NEO-DARWINISM theory and the nature laws. In fact the new algorithm will let the species agents able to count their own GA parameters. In order to show D3G2A advantages, the approach and the GGA are applied on the radio link frequency allocation problem (RLFAP). The experimental comparison is provided

Inspired by the guided genetic algorithm (GGA) and by the dynamic distributed double guided genetic algorithm for Max/spl I.bar/CSPs, D/sup 3/G/sup 2/A is a new multi-agent approach which addresses additive constraint satisfaction problems (/spl Sigma/CSPs). This algorithm consists of agents dynamically created and cooperating in order to solve the problem. Each agent performs its own GA, guided by both the template concept and the min-conflict-heuristic. In one hand, genetic algorithms (GAs) efficiency provides good solution quality for additive CSPs and, in the other hand, multi-agent principles reduces GA temporal complexity. First, our approach is enhanced by a new parameter called guidance operator. The latter allows not only diversification but also an escaping from local optima. In the second step, the performed GAs no longer have the same cross-over and mutation probabilities. This is done on the basis of NEO-DARWINISM theory and the nature laws. In fact the new algorithm let the species agents able to count their GA parameters. In order to show D/sup 3/G/sup 2/A advantages, experimental comparison with GGA is provided.

This paper explains how to optimize the workplans of mobile agents (MAs) in order to enhance their performance. A MA travels through transport network looking for useful information and services. This information should be available daily to assist travelers to use multimodal transportation system. Our objective is to maximize the number of satisfied transport travelers. In other words, we attempt to increase the satisfaction of transport customers during their travels. So, we intend to optimize task assignment to a set of servers which can offer information with preset cost and processing time. With this intention, we adopted an approach based on evolutionary programs in order to solve our combinatorial problem. Therefore, it is very important to design an efficient representational scheme of a chromosome and develop effective genetic operators. We create a new representation of the chromosome where we integrated the constraints of our problem

The vehicle routing problem with time windows (VRPTW) is a well known combinatorial optimization problem often met in many fields of industrial applications. We are interested in a coalition based multiagent model (Coal-VRP) for the VRPTW. However, this model presents some drawbacks due to its spatial and temporal complexity. In order to overcome these drawbacks while maintaining the solution quality, we propose in this paper a new version of this model called DyCoal-VRP. It is essentially based on dynamic generation of coalitions. An experimental validation of our model is achieved on the base of Solomon's benchmark.

The aim of the paper is to show the advantage of the distributed approach based on an optimization process like simulated annealing. This advantage is exhibited through the example of constraint satisfaction problems. Since most of these problems are currently dynamic, the paper presents an extension of the basic model, which has already been developed and successfully experimented with (K. Ghedira, 1994). Thus, two revision mechanisms, based on distributed simulated "re-annealing", are proposed and compared from the efficiency, stability and optimality points of view.>