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Mathematical Problems in Engineering
Volume 2014, Article ID 671872, 11 pages
Research Article

Fitness Estimation Based Particle Swarm Optimization Algorithm for Layout Design of Truss Structures

1Research Centre of Satellite Technology, Harbin Institute of Technology, Harbin 150001, China
2Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China

Received 2 August 2014; Revised 8 September 2014; Accepted 9 September 2014; Published 29 September 2014

Academic Editor: Shifei Ding

Copyright © 2014 Ayang Xiao et al. 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.


Due to the fact that vastly different variables and constraints are simultaneously considered, truss layout optimization is a typical difficult constrained mixed-integer nonlinear program. Moreover, the computational cost of truss analysis is often quite expensive. In this paper, a novel fitness estimation based particle swarm optimization algorithm with an adaptive penalty function approach (FEPSO-AP) is proposed to handle this problem. FEPSO-AP adopts a special fitness estimate strategy to evaluate the similar particles in the current population, with the purpose to reduce the computational cost. Further more, a laconic adaptive penalty function is employed by FEPSO-AP, which can handle multiple constraints effectively by making good use of historical iteration information. Four benchmark examples with fixed topologies and up to 44 design dimensions were studied to verify the generality and efficiency of the proposed algorithm. Numerical results of the present work compared with results of other state-of-the-art hybrid algorithms shown in the literature demonstrate that the convergence rate and the solution quality of FEPSO-AP are essentially competitive.