Abstract. The purpose of this work is to present a model for portfolio multi-optimization, in which distributions are compared on the basis of tow statistics: the expected value and the Conditional Value-at-Risk (CVaR), to solve such a problem many authors have developed several algorithms, in this work we propose to find the efficient boundary by using the Normal Boundary Intersection approach (NBI) based on our proposed hybrid method SASP, since the considered problem is multi-objective, then we find the Kalai-smorodinsky solution.

Abstract. In the present paper, a general multiobjective optimization problem is stated as a Nash game. In the nonrestrictive case of two objectives, we address the problem of the splitting of the design variable between the two players. The so-called territory splitting problem is solved by means of an allocative approach. We propose two algorithms in order to find fair allocation tables.

Image inpainting is an important research area in image processing. Its main purpose is to supplement missing or damaged domains of images using information from surrounding areas. This step can be performed by using nonlinear diffusive filters requiring a resolution of partial differential evolution equations. In this paper, we propose a filter defined by a partial differential nonlinear evolution equation with spatial fractional derivatives. Due to this, we were able to improve the performance obtained by known inpainting models based on partial differential equations and extend certain existing results in image processing. The discretization of the fractional partial differential equation of the proposed model is carried out using the shifted Grünwald–Letnikov formula, which allows us to build stable numerical schemes. The comparative analysis shows that the proposed model produces an improved image quality better or comparable to that obtained by various other efficient models known from the literature.

International audience We are interested here, in multi-criteria optimization problem using game theory. This problem will be treated by using a new algorithm for the splitting of territory in case of concurrent optimization, which presents a new formulation of Nash games between two players using two tables of allocations. Each player minimizes his cost function using the variables allocated by his own table. The two tables are given by an iterative algorithm. An image processing problem is addressed by using the proposed algorithms. On s’intéresse, dans ce travail, à un problème d’optimisation multi-critère en utilisant la théorie des jeux. Ce problème est traité en utilisant de nouveaux algorithmes pour le partage de territoire dans le cas d’une optimisation concourante. Il s’agit de présenter une formulation de jeux de Nash entre deux joueurs en utilisant deux tableaux d’allocation. Chaque joueur minimise sa fonction coût en agissant sur les variables allouées par son propre tableau. Les deux tableaux sont à construire grâce à un algorithme itératif. Une application de ces algorithmes à un problème de traitement d’images est considérée.

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