Estimation de mouvement par les techniques métaheuristiques

L’estimation de mouvement est un processus qui consiste à estimer, à partir d’une séquence d’images, le mouvement apparent des objets composant une scène tridimensionnelle. La méthode de mise en correspondance de blocs : block matching (BM) est la méthode d’estimation de mouvement la plus utilisée. L’aspect important de cette méthode est l’utilisation des stratégies de recherche intelligentes afin d’obtenir une précision d’estimation de mouvement élevée avec une complexité de calcul réduite. Dans cette thèse, nous nous intéressons aux techniques de BM basées sur les métaheuristiques, deux nouveaux algorithmes sont proposés. Dans la première partie de cette thèse, nous proposons un algorithme de BM basé sur la recherche fractale stochastique. Les résultats de simulation obtenus montrent la supériorité de l’algorithme proposé par rapport aux autres techniques BM en termes de précision d’estimation de mouvement et complexité de calcul. Dans une deuxième partie, nous présentons la technique métaheuristique que nous avons développée, nommée optimisation à base de LBP : local binary pattern optimizer (LBPO), elle est inspirée par le concept de base du descripteur LBP. Nous validons la méthode LBPO avec des fonctions de test connues, puis nous appliquons cette méthode au problème de BM. Les résultats expérimentaux montrent l’efficacité de la méthode proposée

Application of Stand-PSO Technique for Optimization Cameras’ 2D Dispositions in a MoCap system

In this paper, a detailed study of the Particle Swarm Optimization (PSO) technique is given in its standard version to solve a network camera placement problem and to ensure the coverage of a reflector point by, at least, two cameras in each frame of a motion sequence of an object in movement in a MoCap (Motion Capture) system. Solving the problem is by optimizing the extrinsic camera parameters for the whole network. The interest of this study is to determine the advantages and limits of this metaheuristic. Simulation results for 2D scenarios showed the effectiveness of this technique when considering all continuous space and the presence of obstacles

A new predictive medical approach based on data mining and Symbiotic Organisms Search algorithm

International audienceHandling very large data, in order to make the best decision, is only possible through an extraction of knowledge. Data mining has become a widely used process in data analytics to extract the most important knowledge for predictive decision making. One of the important types of data mining is clustering mechanism ; its purpose is dividing data into a set of clusters with very large data, the numbers of parameters are very high, and the clustering problem is more difficult. Metaheuristics have been widely used in clustering; they can provide satisfactory solutions for complex problems. The main objective of this paper is to propose a new clustering algorithm based on a metaheuristic technique called Symbiotic Organisms Search (SOS), it was inspired from a biological process, and it simulates the symbiotic interaction between organisms of the same population. The SOS method is used to find the optimal centers of a number of clusters, as a supervised data mining technique. Experimental results have been performed through two phases. Firstly, the SOS technique is benchmarked with six well-known test functions. Secondly, different medical datasets have been used to test our proposed clustering method based on SOS, and show its credibility of treatment. ARTICLE HISTOR

Smart Breast Cancer Prediction Using Data Mining Process

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Stability and Stabilization of TS Fuzzy Systems via Line Integral Lyapunov Fuzzy Function

This paper is concerned with the stability and stabilization problem of a Takagi-Sugeno fuzzy (TSF) system. Using a non-quadratic function (well-known integral Lyapunov fuzzy candidate (ILF)) and some lemmas, new sufficient conditions are established as linear matrix inequalities (LMIs), which are solved with a stochastic fractal search (SFS). The main advantage of the technique used is its small conservatives. Motivated by the mean value theorem, a state feedback controller based on a non-quadratic Lyapunov function is designed. Unlike other approaches based on poly-quadratic Lyapunov candidates, stability conditions of the closed loop are obtained in LMI regions. It is important to highlight that the time derivatives of membership functions do not appear in the used line integral Lyapunov function, which is the well-known problem of poly-quadratic Lyapunov functions. A numerical example is given to show the advantages and the utility of the integral Lyapunov fuzzy candidate, which provides a wider feasibility region than other Lyapunov functions

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

Abstract In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability