Papers by Author: Rachid Ellaia

Abstract: In this paper, a new hybrid method of optimization by the heuristics algorithms to evaluate the reliability of the electronic card by simulating its thermo-mechanical behavior is presented. A model of simulation by finite element is developed to consider the maximal deformations due to temperature; a mechanico- computing coupling is used to find the optimal structure. Embedded electronic systems are playing a very important role in several areas, such as in automotive, aerospace, telecommunications and medical sectors. To properly perform their functions, electronic systems must be reliable [18].This powerful and robust algorithm which is based on hybridization of Differential Evolutionary algorithm with Particle Swarm Optimization (PSO) gives performance results [7].

Abstract: Embedded electronic systems are playing a very important role in several areas, such as in automotive, aerospace, telecommunications and medical sectors. To properly perform their functions, electronic systems must be reliable [2. So in this paper, we present a new hybrid method of optimization by the heuristics algorithms to evaluate the reliability of the electronic card by simulating its thermo-mechanical behavior. A model of simulation by finite element is developed to consider the maximal deformations due to the temperature; a mechanico-computing coupling is used to find the optimal structure. This powerful and robust algorithm which is based on hybridation of Genetic algorithm with Particle swarm optimization PSO gives performance results.

Abstract: In this paper, we present a new hybrid algorithm which is a combination of a hybrid genetic algorithm and particle swarm optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO) for the global optimization. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. The performance of the two algorithms has been evaluated using several experiments.

Abstract: Most optimization problems, particularly those in engineering design, require the simultaneous optimization of more than one objective function. In this context, the solutions of these problems are based on the Pareto frontier construction. Substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The Normal Boundary Intersection (NBI) is a recent contribution that generates a well-distributed Pareto frontier efficiently. Nevertheless, this method should be combined with a global optimization method to ensure the convergence to the global Pareto frontier. This paper proposes the NBI method using Adaptive Simulated Annealing (ASA) algorithm, namely NBI-ASA as a global nonlinear multi-objective optimization method. A well known benchmark multi-objective problem has been chosen from the literature to demonstrate the validity of the proposed method, applicability of the method for structural problems has been tested through a truss problem and promising results were obtained. The results indicate that the proposed method is a powerful search and multi-objective optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms.

Abstract: This paper presents a new methodology for the Reliability Based Particle Swarm Optimization with Simulated Annealing. The reliability analysis procedure couple traditional and modified first and second order reliability methods, in rectangular plates modelled by an Assumed Modes approach. Both reliability methods are applicable to the implicit limit state functions through numerical models, like those based on the Assumed Mode Method. For traditional reliability approaches, the algorithms FORM and SORM use a Newton-Raphson procedure for estimate design point. In modified approaches, the algorithms are based on heuristic optimization methods such as Particle Swarm Optimization and Simulated Annealing Optimization. Numerical applications in static, dynamic and stability problems are used to illustrate the applicability and effectiveness of proposed methodology. These examples consist in a rectangular plates subjected to in-plane external loads, material and geometrical parameters which are considered as random variables. The results show that the predicted reliability levels are accurate to evaluate simultaneously various implicit limit state functions with respect to static, dynamic and stability criterions.

Abstract: In this paper, we present a probability study about spring of clutch structure. In the structure problems, the randomness and the uncertainties of the distribution of the structural parameters are a crucial problem. In the case of Reliability Based Design Optimization (RBDO), it is the objective is to play a dominant role in the structural optimization problem introducing the reliability concept. The RBDO problem is often formulated as a minimization of the initial structural cost under constraints imposed on the values of elemental reliability indices corresponding to various limit states. This paper proposes a new method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA) and RBDO. MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence algorithm inspired by social behavior simulations of bird flocking. Numerical results show the robustness of the MPSO-SA algorithm and RBDO.

Abstract: The aim of this paper is to study the implementation of an efficient and reliable methodology for shape optimization problems where the objective function and constraints are not known explicitly and are dependent on the Finite Element Analysis (FEA). It is based on the Simultaneous Perturbation Stochastic Approximation (SPSA) method for solving unconstrained continuous optimization problems. We also propose Penalty SPSA (PSPSA) for solving constrained optimization problems, the constraints are handled using exterior point penalty functions within an algorithm that combines SPSA and exact penalty transformations. This paper presents a new structural optimization methodology that combines shape optimization, geometric modeling, FEA and PSPSA method to successfully optimize structural optimization problems. Several tests have been performed on some well known benchmark functions to demonstrate the robustness and high performance of the suggested methodology. In addition, an illustrative two-dimensional structural problem has been solved in a very efficient way. The numerical results demonstrate the robustness and high performance of the suggested methodology for structural optimization problems.

Abstract: In structural design optimization, numerical techniques are increasingly used. In typical structural optimization problems there may be many locally minimum configurations. For that reason, the application of a global method, which may escape from the locally minimum points, remain essential. In this paper, a new hybrid simulated annealing algorithm for global optimization with constraints is proposed. We have developed a new algorithm called Adaptive Simulated Annealing algorithm (ASA); ASA is a series of modifications done to the Basic Simulated Annealing algorithm ( BSA) that gives the region containing the global solution of an objective function. In addition, the stochastic method Simultaneous Perturbation Stochastic Approximation (SPSA), for solving unconstrained optimization problems, is used to refine the solution. We also propose Penalty SPSA (PSPSA) for solving constrained optimization problems. The constraints are handled using exterior point penalty functions. The proposed method is applicable for any problem where the topology of the structure is not fixed, it is simple and capable of handling problems subject to any number of nonlinear constraints. Extensive tests on the ASA as a global optimization method are presented, its performance as a viable optimization method is demonstrated by applying it first to a series of benchmark functions with 2 - 30 dimensions and then it is used in structural design to demonstrate its applicability and efficiency. It is found that the best results are obtained by ASA compared to those provided by the commercial software ANSYS.

Abstract: The aim of this paper is to present a new methodology for the evaluation of the statistical proprieties of the response of structures, based on The Finite Element Analysis (FEA) coupled with the Probabilistic Transformation Method (PTM). Uncertainty modelling with random variables motivates the adoption of advanced PTM for reliability analysis to solve problems of mechanical systems. The PTM is readily applicable in the case where the expression between input and output of structures are available in explicit analytical form. However, the situation is much more involved when it is necessary to perform the evaluation of implicit expression between input and output of structures through numerical models. For this we propose technique that combines the FEA software, and the PTM program to evaluate the Probability Density Function (PDF) of the response where the expression between input and output of structures is implicit. This technique is based on the numerical simulations of the FEA and the PTM by making an interface between Finite Element software and Matlab. Some problems of structures are treated in order to demonstrate the applicability of the proposed technique.