Unidimensional search for solving continuous highdimensional optimization problems

Abstract-This paper presents a performance study of two versions of a unidimensional search algorithm aimed at solving high-dimensional optimization problems. The algorithms were tested on 11 scalable benchmark problems. The aim is to observe how metaheuristics for continuous optimization problems respond with increasing dimension. To this end, we report the algorithms' performance on the 50, 100, 200 and 500-dimension versions of each function. Computational results are given along with convergence graphs to provide comparisons with other algorithms during the conference and afterwards

Convergence proof of an enhanced particle swarm optimisation method integrated with evolutionary game theory

This paper proposes an enhanced Particle Swarm Optimisation (PSO) algorithm and examines its performance. In the proposed PSO approach, PSO is combined with Evolutionary Game Theory to improve convergence. One of the main challenges of such stochastic optimisation algorithms is the difficulty in the theoretical analysis of the convergence and performance. Therefore, this paper analytically investigates the convergence and performance of the proposed PSO algorithm. The analysis results show that convergence speed of the proposed PSO is superior to that of the Standard PSO approach. This paper also develops another algorithm combining the proposed PSO with the Standard PSO algorithm to mitigate the potential premature convergence issue in the proposed PSO algorithm. The combined approach consists of two types of particles, one follows Standard PSO and the other follows the proposed PSO. This enables exploitation of both diversification of the particles’ exploration and adaptation of the search direction

A Two-Step Optimisation Method for Dynamic Weapon Target Assignment Problem

International audienceno abstrac

An enhanced particle swarm optimization method integrated with evolutionary game theory

This paper describes a novel particle swarm optimizer algorithm. The focus of this study is how to improve the performance of the classical particle swarm optimization approach, i.e., how to enhance its convergence speed and capacity to solve complex problems while reducing the computational load. The proposed approach is based on an improvement of particle swarm optimization using evolutionary game theory. This method maintains the capability of the particle swarm optimizer to diversify the particles' exploration in the solution space. Moreover, the proposed approach provides an important ability to the optimization algorithm, that is, adaptation of the search direction, which improves the quality of the particles based on their experience. The proposed algorithm is tested on a representative set of continuous benchmark optimization problems and compared with some other classical optimization approaches. Based on the test results of each benchmark problem, its performance is analyzed and discussed

ADAPTATION AUX PROBLEMES A VARIABLES CONTINUES DE PLUSIEURS METAHEURISTIQUES D'OPTIMISATION COMBINATOIRE (LA METHODE TABOU, LES ALGORITHMES GENETIQUES ET LES METHODES HYBRIDES. APPLICATION EN CONTROLE NON DESTRUCTIF)

LES METAHEURISTIQUES - PRINCIPALEMENT LE RECUIT SIMULE, LA METHODE DE RECHERCHE TABOU, LES ALGORITHMES GENETIQUES - SONT CONSIDEREES COMME DES METHODES EFFICACES POUR LA RESOLUTION DE PROBLEMES D'OPTIMISATION COMBINATOIRES. LE TRAVAIL PRESENTE DANS LE CADRE DE CETTE THESE CONSISTE A ADAPTER CES METHODES EN VUE DU TRAITEMENT DES FONCTIONS A VARIABLES CONTINUES, A LES REUNIR DANS UN MEME ENVIRONNEMENT, AFIN DE COMPARER LEURS EFFICACITES, ET A LES APPLIQUER A PLUSIEURS PROBLEMES RELEVANT DU CONTROLE NON DESTRUCTIF PAR COURANTS DE FOUCAULT. NOUS AVONS D'ABORD PROPOSE UNE STRATEGIE EFFICACE DE DISCRETISATION DES VARIABLES, NOUS AVONS DEFINI LA NOTION DE VOISINAGE, ET, POUR CHACUNE DES METHODES DEVELOPPEES, NOUS AVONS EXPLOITE DEUX CONCEPTS : LA DIVERSIFICATION ET L'INTENSIFICATION. LA DIVERSIFICATION PERMET DE BIEN COUVRIR L'ESPACE DES SOLUTIONS, ET DE DETERMINER LES ZONES PROMETTEUSES. L'INTENSIFICATION PERMET D'APPROFONDIR LA RECHERCHE DANS CHACUNE DES ZONES PROMETTEUSES LOCALISEES. NOUS AVONS D'ABORD DEVELOPPE DEUX NOUVELLES METHODES ; LA PREMIERE EST INSPIREE DE LA METHODE DE LA RECHERCHE TABOU, LA SECONDE EST UNE ADAPTATION DES ALGORITHMES GENETIQUES. PUIS NOUS AVONS PERFECTIONNE UN ALGORITHME DE RECUIT SIMULE ADAPTE AUX PROBLEMES A VARIABLES CONTINUES. AFIN D'ACCELERER LA CONVERGENCE DE CES METHODES PURES, NOUS LES AVONS COUPLEES AVEC UNE METHODE DE RECHERCHE LOCALE. NOUS AVONS, A CETTE FIN, MODIFIE LES PHASES D'INTENSIFICATION, EN UTILISANT LA METHODE DU POLYTOPE DE NELDER-MEAD, ET NOUS AVONS AINSI OBTENU TROIS METHODES HYBRIDES. NOUS AVONS REUNI TOUTES CES METHODES DANS UN MEME LOGICIEL, QUE NOUS AVONS APPELE OPTIM. CE LOGICIEL A ETE DEVELOPPE EN PROGRAMMATION ORIENTEE OBJET, ET IMPLEMENTE EN C + +, PUIS EN LANGAGE MATLAB. EN COLLABORATION AVEC LE C.E.A., NOUS AVONS APPLIQUE LES METHODES DEVELOPPEES A L'OPTIMISATION DE CERTAINES FONCTIONS UTILISEES POUR LA CARACTERISATION DE MODELES D'INVERSION, EN CONTROLE NON DESTRUCTIF PAR COURANTS DE FOUCAULT.CERGY PONTOISE-BU Neuville (951272102) / SudocSudocFranceF

Transformation-Based Approach to Security Verification for Cyber-Physical Systems

International audienc

Model‐driven architecture based security analysis

International audienceAbstract This paper proposes a Model‐Driven Architecture approach for the development of an embedded system validation platform namely Model‐Based Security Analysis for Embedded Systems (MBSAES). The security properties are formally modeled and verified at an early stage of the design process of the system, which helps to reduce late errors and development time. A separation of the attack scenarios and the system design from the implementation details has been respected. To transform semi‐formal models from SysML to NuSVM model checking platform, two Model‐to‐Text, horizontal and exogenous transformations have been implemented. The first one employs a programming approach with Java to create a Computational Tree Logic specification from an Extended Attack Tree, whereas the second one uses a template approach with Acceleo to generate NuSMV code from SysML structural and behavioral models. To illustrate our approach, a case study, involving attacks aiming to unlock car door systems, via signal jamming and code replaying, is considered. The results of this research will contribute to the automatic validation of system designs against security vulnerabilities via a database of extended attack trees building from existing atomic attacks