Abstract

We propose a recommender which deals with the suggestion of
suitable supplying opportunities. The established system is addressed to the
handicraft women communities. It targets to respond to their needs and fit their
expectations. Indeed, the recommendation mechanism is based on the final
users profiles, preferences and constraints. Moreover, the adopted
recommendation strategy is hybrid. It includes both the knowledge and the
demographic approaches for better performance. Additionally, we proposed
two recommendation algorithms in order to select and rank suitable suppliers.
In order to validate our approach we designed and developed a context
ontology based multi-agent system. Technically, we developed a J2EE
application based on the JSF technology. Moreover, we introduced interesting
experimentation results showing that our system is accurate and novel.

Abstract

Task scheduling problem has attracted a lot of attention since it plays a key role to improve the performance of any distributed system. This is again more challenging, especially for multi-cloud computing environment, mainly based on the nature of the multi-cloud to scale dynamically and due to heterogeneity of resources which add more complexity to the scheduling problem. In this paper, we propose, for the first time, a new Hybrid Bi-level optimization model named HB-DBoTSP to solve the Dynamic Bag-of-Tasks Scheduling Problem (DBoTSP) in heterogeneous multi-cloud environment. The proposed model aims to minimize the makespan and the execution cost while taking into consideration budget constraints and guaranteeing load balancing between Cloud’s Virtual Machines. By performing experiments on synthetic data sets that we propose, we demonstrate the effectiveness of the algorithm.

Abstract

Vehicular Ad Hoc Networks (VANETs) are a particular class of Mobile Ad Hoc Networks (MANETs). The VANETs provide wireless communication among vehicles and vehicle-to-road-side units. Even though the VANETs are a specific type of MANETs, a highly dynamic topology is a main feature that differentiates them from other kinds of ad hoc networks. As a result, designing an efficient routing protocol is considered a challenge.  The performance of vehicle-to-vehicle communication depends on how better the routing protocol takes in consideration the particularities of the VANETs. Swarm Intelligence (SI) is considered as a promising solution to optimize vehicular communication costs. In this paper, we explore the SI approach to deal with the routing problems in the VANETs. We also evaluate and compare two swarming agent-based protocols using numerous QoS parameters, namely the average end-to-end delay and the ratio packet loss which influence the performance of network communication.

Abstract

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

Abstract

Abstract

The paradigm of Social Internet of Things (SIoT) incorporates the concepts of social networking in the Internet of Things (IoT). The idea of SIoT is to allow objects to autonomously establish social relationships, which may facilitate network navigability and the discovery of information and services. The characteristics of the IoT such as the heterogeneity and the dynamicity of the network and the social relationships between devices lead to several challenges including how to build a reliable network. In this paper, we propose an adaptive trust management model that helps nodes seeking trusted service providers. The trustworthiness of a service provider is assessed on the basis of its past experiences with the requestor and of the recommendations of the requester’s neighbors. In our trust model, the trust parameters evolve dynamically in response to the change of the network context, the type of the demanded service and the nature of the relationships between the different nodes. The experiment results show that our proposed model achieves high accuracy and it is proved to be resilient against common attacks.

Abstract

Multi-objective Bi-Level Optimization Problem (MBLOP) is defined as a mathematical program where one multi-objective optimization task is constrained with another one. In this way, the evaluation of a single upper level solution necessitates the evaluation of the whole lower level problem. This fact brings new complexities to the bi-level framework, added to the conflicting objectives and their evaluation which need a large number of Function Evaluations (FEs). Despite the number of works dedicated to solve bi-level optimization problems, the number of methods applied to the multi-objective combinatorial case is much reduced. Motivated by these observations, we propose in this paper an approximation-based version of our recently proposed Bi-level Multi-objective Chemical Reaction Optimization (BMCRO), which we called BMCROII. The approximation technique is adopted here as a surrogate to the lower level leading then to generate efficiently the lower level optimality. Our choice is justified by two main arguments. First, BMCRO applies a Quick Non-Dominated Sorting Algorithm (Q-NDSA) with quasi-linear computational time complexity. Second, the number of FEs savings gained by the approximation technique can hugely improve the whole efficiency of the method. The proposed algorithm is applied to a new multi-objective formulation of the well-known Bi-level Multi Depot Vehicle Routing Problem (BMDVRP). The statistical analysis demonstrates the outperformance of our algorithm compared to prominent baseline algorithms available in literature. Indeed, a large number of savings are detected which confirms the merits of our proposal for solving such type of NP-hard problems.

Abstract

Nowadays, positioning technologies have become widely available providing then large datasets of individuals’ mobility data. Actually, annotating raw traces with contextual information brings semantics to them and then provides a better understanding of people behavior. To do so, literature work explored novel techniques to enrich raw mobility data with contextual information using either geographic context represented by landmarks/points of interest or widely used social media feeds. Accordingly, in this work, a novel approach integrating three data sources: raw mobility data, geographic information and social media feeds for a two-fold trajectory semantic annotation process is presented. In a first step, structured trajectories are constructed using geographic information. Later, the former are annotated by event-related words grasped from social media. Indeed, combining both data sources could result in a more complete annotation of trajectories. The proposed approach is experimented and evaluated on datasets of tourists in Kyoto. Results showed that the proposed approach quantitatively performed well compared to previous work in terms of precision of annotation words that maintained  when recall reached 50%, while improving its quality by consolidating both sources of semantics.

Abstract

In this paper, a novel dynamic agent routing simulator for vehicular ad-hoc networks (DARSV) is presented. The main purpose of DARSV simulator is to realise a successful large-scale simulation of agent based routing approach in vehicular networks. To conduct this goal, the proposed simulator combines the Java Agent DEvelopment (JADE) which is a powerful multi-agent system (MAS) framework with the dynamic ad hoc routing simulator (DARS) that takes into account the dynamic nature of environment networks. The simulation results are discussed to evaluate the efficiency and the performance of the proposed simulator.

Abstract

This study examines a two-stage three-machine flow-shop assembly scheduling model in which job processing time is considered as a mixed function of a controlled truncation parameter with a sum-of-processing-times-based learning effect. However, the truncation function is very limited in the two-stage flow-shop assembly scheduling settings. To overcome this limitation, this study investigates a two-stage three-machine flow-shop assembly problem with a truncation learning function where the makespan criterion (completion of the last job) is minimized. Given that the proposed model is NP hard, dominance rules, lemmas and a lower bound are derived and applied to the branch-and-bound method. A dynamic differential evolution algorithm, a hybrid greedy iterated algorithm and a genetic algorithm are also proposed for searching approximate solutions. Results obtained from test experiments validate the performance of all the proposed algorithms.