Particle Swarm vs. Evolutionary Optimization Techniques in a Multiobjective Framework for Damage Identification
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.
F. Chu, H. Ouyang, V. Silberschmidt, L. Garibaldi, C.Surace, W.M. Ostachowicz and D. Jiang
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