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Journal of EngineeringVolume 2013 2013, Article ID 435104, 13 pages

Research Article

ENEA C.R. Frascati, Via E. Fermi 45, 00044 Frascati, Italy

LIDIC Research Group, Universidad Nacional de San Luis, Ej. de los Andes 950, 5700 San Luis, Argentina

Received 31 December 2012; Accepted 10 February 2013

Academic Editor: Yangmin Li

Copyright © 2013 Giordano Tomassetti and Leticia Cagnina. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In many disciplines, the use of evolutionary algorithms to perform optimizations is limited because of the extensive number of objective evaluations required. In fact, in real-world problems, each objective evaluation is frequently obtained by time-expensive numerical calculations. On the other hand, gradient-based algorithms are able to identify optima with a reduced number of objective evaluations, but they have limited exploration capabilities of the search domain and some restrictions when dealing with noncontinuous functions. In this paper, two PSO-based algorithms are compared to evaluate their pros and cons with respect to the effort required to find acceptable solutions. The algorithms implement two different methodologies to solve widely used engineering benchmark problems. Comparison is made both in terms of fixed iterations tests to judge the solution quality reached and fixed threshold to evaluate how quickly each algorithm reaches near-optimal solutions. The results indicate that one PSO algorithm achieves better solutions than the other one in fixed iterations tests, and the latter achieves acceptable results in less-function evaluations with respect to the first PSO in the case of fixed threshold tests.

Autor: Giordano Tomassetti and Leticia Cagnina



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