Abstract

The Vehicular ad -hoc networks (VANETs) are a specific type of Mobile ad-hoc networks (MANETs). However, the main problem related to it is the potential high speed of moving vehicles. This special property causes frequent changing in network topology and instability of communication routes. Consequently, some of the challenges that researchers focus on are routing protocols for VANETs. They have proved that the existing MANET proactive routing protocols are the most used for vehicular communication. Yet, they are not as adequate as they are for VANETs. The main problem with these protocols in dynamic environment is their route instability. This paper combines multi-agent system approach and PSO algorithm to solve the above mentioned problems. We carried out a set of simulations tests to evaluate the performance of our scheme. The simulation part shows promising results regarding the adoption of the proposed scheme.

Abstract

This paper provides an agent technology for
a decision support system. This system is designed to
detect and regulate the traffic of multimodal public
transport when many disturbances come simultaneously.
The objective of this system is to optimize the regulation
action by learning technique of regulator. The goal of this
research is to improve the quality of public transport
service provided to users and respect the use rules (safety
rules, business rules, commercial rules, etc.). So, to
improve the quality service of the user, we have to
optimize simultaneously several criteria like punctuality,
regularity and correspondence in disturbance case. In
this paper, we focus primarily on a multi agent system for
optimizing and learning of Regulation Support System of
a Multimodal Public Transport (RSSPT). We have
validated our strategy by simulating situation related to
existing transportation system.

Abstract

In recent years, the Object Management Group (OMG)  has  focused  its  work  to  improve  the  business process modeling on multiple axes. The investigation in the domain  of  the  decision-making  has  resulted  in  its externalization through the invention of the Decision Model and Notation  (DMN). The latter, as presented  by OMG, is designed  as  a  supplement  to the  Business  Process  Model and  Notation  (BPMN),  to  model  decision-making  in business process.  DMN  covers  several  aspects  of  decision-making, but some factors are not explicitly mentioned, such as  harmonization,  synergy  and  uncertainty.  Since  the decision is based on modeling, serialization and integration of business  rules in the  business  process,  several questions arise  around  these problems.

In  this paper,  we  study the structure  of  business  rules  favoring  harmonization  of decisions  and  we  propose  an  additional  approach  for business  rules  serialization  through  NoSQL  databases, specifically MongoDB as a Document-Oriented database.

Abstract

Nowadays, one of the companies challenges is to benefit from their Business Intelligence (BI) projects and not to see huge investments ruined. To address problems related to the modelling of these projects and the management of their life-cycle, Enterprise Architecture (EA) Frameworks are considered as an attractive alternative to strengthen the Business-IT alignment. Business Process Model and Notation (BPMN) represents a pillar of these Frameworks to minimize the gap between the expectations of managers and delivered technical solutions. The importance of decision-making in business process has led the Object Management Group (OMG) to announce its new standard: Decision Model and Notation (DMN). In this paper, we propose the BPMN Decision Footprint (BPMNDF), which is a coupling of a BPMN with a novel DMN version. This enhancement has an additional component as a repository of all decisions along the process, used in order to ensure the harmony of decision-making.

Abstract

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

Abstract

Bi-level optimization problems are a class of challenging optimization problems, that contain two levels of optimization tasks. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. In recent decades, it is observed that many efficient optimizations using modern advanced EAs have been achieved via the incorporation of domain specific knowledge. In such a way, the embedment of domain knowledge about an underlying problem into the search algorithms can enhance properly the evolutionary search performance. Motivated by this issue, we present in this paper a Memetic Evolutionary Algorithm for Bi-level Combinatorial Optimization (M-CODBA) based on a new recently proposed CODBA algorithm with transfer learning to enhance future bi-level evolutionary search. A realization of the proposed scheme is investigated on the Bi-CVRP and Bi-MDVRP problems. The experimental studies on well established benchmarks are presented to assess and validate the benefits of incorporating knowledge memes on bi-level evolutionary search. Most notably, the results emphasize the advantage of our proposal over the original scheme and demonstrate its capability to accelerate the convergence of the algorithm.

Abstract

Vehicular Ad-hoc Networks (VANETs) are known as a special type of Mobile Ad-hoc Networks (MANETs) specialized in vehicular communications. These networks are based on smart vehicles and basestations, which share data by means of wireless communications. To route these information, a routing protocol is required. Since the VANETs have a particular network features as rapidly changeable topology, designing an efficient routing scheme is a very hard task. In this paper, we mainly focus on surveying new routing protocols dedicated to VANETs. We present unicast, multicast and broadcast protocols. The experimental results are discussed to evaluate the performance of the presented methods.

Abstract

Vehicular Ad-hoc Network (VANET) is a sub-family of Mobile Ad-hoc Network (MANET).The means goal of VANET is to provide communications between nearby nodes or between nodes and fixed infrastructure. Despite that VANET is considered as a subclass of MANET, it has for particularity the high mobility of vehicles producing the frequent changes of network topology that involve changing of road, varying node density and locations of vehicles existing in this road. That‘s why, the most proposed clustering algorithms for MANET are unsuitable for VANET. Various searches have been recently published deal with clustering for VANETs. But most of them are focused on minimizing network overhead value, number of created clusters and had not considered the vehicles interests which defined as any related data used to differentiate vehicle from another (such as traffic congestion, looking for free parking space etc). In this paper, we propose a novel clustering algorithm based on agent technology to solve the problems mentioned above and improve routing in VANET. Experimental part show promising results regarding the adoption of the proposed approach.

Abstract

Job shop scheduling problems (JSSP) are among the most intensive combinatorial problems studied in literature. The flexible job shop problem (FJSP) is a generalization of the classical JSSP where each operation can be processed by more than one resource. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper investigates the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a genetic algorithm (GA) to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our GA against the available ones in terms of solution quality.

Abstract

Emotions shape human behaviors particularly during stressful situations. This paper addresses this challenging issue by incorporating coping mechanisms into an emotional agent. Indeed, coping refers to cognitive and behavioral efforts employed by humans to overcome stressful situations. In our proposal, we intend to show the potential of the integration of coping strategies to produce fast and human-like behavioral responses in emergency situations. Particularly, we propose a coping model that reveals the effect of agent emotions on their action selection processes.