A New Methodology for the Reliability Based Particle Swarm Optimization with Simulated Annealing

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.

• Periodical: Advanced Materials Research (Volume 274)
• Pages: 101-111
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.274.101
• Online since: July 2011
• Authors: Norelislam Elhami, Rachid Ellaia, Mhamed Itmi
• Keywords: Dynamic, Global Optimization, Hybrid, Particle Swarm Optimization Algorithm (PSO), Reliability Based Design Optimization (RBDO), SA, Vibration

References

[1] Haldar A, Mahadevan S. Reliability Assessment Using Stochastic Finite Element Analysis. John Wiley & Sons, Inc., New York, USA, (2000).

[2] Rojas JE, Viana FAC, Rade D. Natured-inspired Optimization Techniques Applied to Structural Reliability Analysis. 7th Multidisciplinary International Conference Quality and Reliability; March 20-22, 2007; Tangier, Morocco, (2007).

[3] Kaldas MM, Dickinson SM. The Flexural Vibration of Welded Rectangular Plates", Journal of Sound and Vibration. 1981; 75(2): pp.163-178.

DOI: 10.1016/0022-460x(81)90337-0

[4] Fiessler B, Neumann HJ, Rackwitz R. Quadratic Limit States in Structural Reliability. Journal of Engineering Mechanics-ASCE. 1979; Vol. 1095; No. 4; pp.661-676.

DOI: 10.1061/jmcea3.0002512

[5] Mohsine A. Contribution à l'optimisation Fiabiliste en Dynamique des Structures Mécaniques. Thèse de Doctorat, Institut National des Sciences Appliquées de Rouen, France. (2006).

[6] Kennedy J, Eberhart RC. Particle Swarm Optimization. ProceDen Hartog, J. P., 1956; Mechanical Vibrations; 1995; McGraw-Hill, New York, USA.

[7] B. Bochenek _ P. Fory_s, Structural optimization for post buckling behavior using particle swarms, Struct Multidisc Optim, 2006 pp.521-531.

DOI: 10.1007/s00158-006-0044-8

[8] J. Behnamian S.M. T Fatemi Ghomi, Development of a PSO-SA hybrid metaheuristic for a new comprehensive regression model to time-series forecasting, Expert Systems with Applications, 2010 pp.974-984.

DOI: 10.1016/j.eswa.2009.05.079

[9] Majid Bahrepour, Elham Mahdipour, Raman Cheloi, and Mahdi Yaghoobi, SUPER SAPSO: A New SA-Based PSO Algorithm, Applications of Soft Computing, 2009 pp.423-430.

DOI: 10.1007/978-3-540-89619-7_41