Particle Swarm vs. Evolutionary Optimization Techniques in a Multiobjective Framework for Damage Identification

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In the context of real-world damage detection problems, the lack of a clear objective function advises to perform simultaneous optimizations of several objectives with the purpose of improving the performance of the procedure. Evolutionary algorithms have been considered to be particularly appropriate to these kinds of problems. However, evolutionary techniques require a relatively long time to obtain a Pareto front of high quality. Particle swarm optimization (PSO) is one of the newest techniques within the family of optimization algorithms. The PSO algorithm relies only on two simple PSO self-updating equations whose purpose is to try to emulate the best global individual found, as well as the best solutions found by each individual particle. Since an individual obtains useful information only from the local and global optimal individuals, it converges to the best solution quickly. PSO has become very popular because of its simplicity and convergence speed. However, there are many associated problems that require further study for extending PSO in solving multi-objective problems. The goal of this paper is to present the first application of PSO to multiobjective damage identification problems and investigate the applicability of several variations of the basic PSO technique. The potential of combining evolutionary computation and PSO concepts for damage identification problems is explored in this work by using a multiobjective evolutionary particle swarm optimization algorithm.

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Periodical:

Key Engineering Materials (Volumes 413-414)

Edited by:

F. Chu, H. Ouyang, V. Silberschmidt, L. Garibaldi, C.Surace, W.M. Ostachowicz and D. Jiang

Pages:

661-668

Citation:

R. Perera et al., "Particle Swarm vs. Evolutionary Optimization Techniques in a Multiobjective Framework for Damage Identification", Key Engineering Materials, Vols. 413-414, pp. 661-668, 2009

Online since:

June 2009

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$41.00

[1] R. Perera and R. Torres, R: Journal of Structural Engineering ASCE , Vol. 132(9) (2006), p.1491.

[2] Y. Haralampidis, C. Papadimitriou and M. Pavlidou: Earthquake Engineering and Structural Dynamics, Vol. 34 (2005), p.665.

[3] R. Perera, A. Ruiz and C. Manzano: Engineering Structures, Vol. 29(10) (2007), p.2540.

[4] C.A. Coello, D.A. Van Veldhuizen and G.B. Lamont, in: Evolutionary algorithms for solving multiobjective problems. (Kluwer Academic Publishers, New York 2002).

[5] C.A. Coello: Recent trends in evolutionary multiobjective optimization. Evolutionary Multiobjective Optimization: Theoretical Advances and Applications( Eds Abraham, Jain and Goldberg, Springer-Verlag London 2005).

DOI: https://doi.org/10.1007/1-84628-137-7_2

[6] J. Kennedy and R.C. Eberhart: Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ (1995), p.1942-(1948).

[7] X. Hu, R. Eberhart and Y. Shi: Recent advances in particle swarm. IEEE Congress on Evolutionary Computation, Portland, Oregon, USA (2004).

[8] C.A. Coello, G.T. Pulido and M.S. Lechuga: Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computations Vol. 8(3) (2004), pp.256-279.

DOI: https://doi.org/10.1109/tevc.2004.826067

[9] J. Lemaitre, in: A course on damage mechanics. (Springer, Berlin 1996).

[10] R.J. Allemang and D.L. Brown: A correlation for modal vector analysis. Proceedings of 1st International Modal Analysis Conference (1982), pp.110-116.

[11] R.C. Eberthart and Y. Shi: Particle swarm optimization: developments, applications and resources. Proceedings of the 2001 Congress on Evolutionary Computation, Seoul Vol. 1 (2001), pp.81-86.

[12] J.N. Morse: Reducing the size of the nondominated set: Pruning by clustering. Computational Operations Research Vol. 7(1-2) (1980).

DOI: https://doi.org/10.1016/0305-0548(80)90014-3

[13] D. Goldberg and J.J. Richardson: Genetic algorithms with sharing for multimodal function optimization. Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms (1987).

DOI: https://doi.org/10.4324/9780203761595

[14] E. Zitzler and L. Thiele: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation Vol. 3(4) (1999), pp.257-271.

DOI: https://doi.org/10.1109/4235.797969