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Keywords: reliability based optimal design, particle swarm optimization, time-varying parameters, truss structures.

Summary

Particle swarm optimization (PSO) is a population-based stochastic optimization technique suitable for global optimization with no need for direct evaluation of gradients. The method mimics the social behaviour of flocks (swarms) of birds and insects [1] and satisfies the five axioms of swarm intelligence, namely; proximity, quality, diverse response, stability and adaptability [2].

In this work, the discrete (binary) version of the algorithm (BPSO) introduced by Kennedy and Eberhart [3] is implemented in the reliability based optimal design (RBOD) of two statically determinate planar trusses is examined, namely of a 25-bar truss and a 30-bar arch. These structures define two types of structural systems carrying vertical loads which represent two different patterns of structural behaviour.

A modified BPSO, based on the work of Fourie and Groenwold [4] is employed. These modifications are mainly concerned with the introduction of time-varying schemes for the inertia parameter and the maximum velocity. Five time-varying schemes are examined. These are; a simple ascending or descending scheme, two periodical schemes and two schemes following the saw-tooth GA [5]. The results obtained from these variants are examined against the results of the BPSO [6].

From the results of the analysis, it becomes evident that the proposed modifications improve considerably the robustness of the algorithm and in particular its exploitation capabilities. For both structures, the best results are obtained with a saw-tooth scheme with gradually increasing maximum velocity and gradually increasing value of the inertia parameter (in synchronization).

References

1: J. Kennedy, R.C. Eberhart, "Particle swarm optimization", In "Proceedings of IEEE International Conference on Neural Networks", 1942-1948, 1995. doi:10.1109/ICNN.1995.488968
2: M.M. Millonas, "Swarms, Phase Transitions and Collective Intelligence", In "Artificial Life III, Santa Fe Institute, Studies in the Sciences of Complexity, XVII", 417-445, 1994.
3: J. Kennedy, R.C. Eberhart, "A discrete binary version of the particle swarm algorithm", In "Proceedings of the Conference on systems, Man, and cybernetics, Orlando USA", IEEE press, Piscataway, N.J., 4104-4108, 1997. doi:10.1109/ICSMC.1997.637339
4: P.C. Fourie, A.A. Groenwold, "The particle swarm optimization algorithm in size and shape optimization", Structural and Multidisciplinary Optimization, 23(4), 259-267, 2002. doi:10.1007/s00158-002-0188-0
5: V.K. Koumousis, C.P. Katsaras "A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance", IEEE Transactions on Evolutionary Computation, 10, 19-28, 2006. doi:10.1109/TEVC.2005.860765
6: C.K. Dimou, V.K. Koumousis, "Reliability based optimal design of truss structures using particle swarm optimization", Journal of Computing in Civil Engineering, ASCE, 23, 100-109, 2009. doi:10.1061/(ASCE)0887-3801(2009)23:2(100)

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 97 PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis, B.H.V. Topping Paper 36 Reliability Based Optimal Design of Truss Structures using Binary Particle Swarm Optimization with Time-Varying Parameters C.K. Dimou and A.E. Charalampakis National Technical University of Athens, Greece doi:10.4203/ccp.97.36 purchase the full-text of this paper Full Bibliographic Reference for this paper C.K. Dimou, A.E. Charalampakis, "Reliability Based Optimal Design of Truss Structures using Binary Particle Swarm Optimization with Time-Varying Parameters", in Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 36, 2011. doi:10.4203/ccp.97.36 Keywords: reliability based optimal design, particle swarm optimization, time-varying parameters, truss structures. Summary Particle swarm optimization (PSO) is a population-based stochastic optimization technique suitable for global optimization with no need for direct evaluation of gradients. The method mimics the social behaviour of flocks (swarms) of birds and insects [1] and satisfies the five axioms of swarm intelligence, namely; proximity, quality, diverse response, stability and adaptability [2]. In this work, the discrete (binary) version of the algorithm (BPSO) introduced by Kennedy and Eberhart [3] is implemented in the reliability based optimal design (RBOD) of two statically determinate planar trusses is examined, namely of a 25-bar truss and a 30-bar arch. These structures define two types of structural systems carrying vertical loads which represent two different patterns of structural behaviour. A modified BPSO, based on the work of Fourie and Groenwold [4] is employed. These modifications are mainly concerned with the introduction of time-varying schemes for the inertia parameter and the maximum velocity. Five time-varying schemes are examined. These are; a simple ascending or descending scheme, two periodical schemes and two schemes following the saw-tooth GA [5]. The results obtained from these variants are examined against the results of the BPSO [6]. From the results of the analysis, it becomes evident that the proposed modifications improve considerably the robustness of the algorithm and in particular its exploitation capabilities. For both structures, the best results are obtained with a saw-tooth scheme with gradually increasing maximum velocity and gradually increasing value of the inertia parameter (in synchronization). References 1 J. Kennedy, R.C. Eberhart, "Particle swarm optimization", In "Proceedings of IEEE International Conference on Neural Networks", 1942-1948, 1995. doi:10.1109/ICNN.1995.488968 2 M.M. Millonas, "Swarms, Phase Transitions and Collective Intelligence", In "Artificial Life III, Santa Fe Institute, Studies in the Sciences of Complexity, XVII", 417-445, 1994. 3 J. Kennedy, R.C. Eberhart, "A discrete binary version of the particle swarm algorithm", In "Proceedings of the Conference on systems, Man, and cybernetics, Orlando USA", IEEE press, Piscataway, N.J., 4104-4108, 1997. doi:10.1109/ICSMC.1997.637339 4 P.C. Fourie, A.A. Groenwold, "The particle swarm optimization algorithm in size and shape optimization", Structural and Multidisciplinary Optimization, 23(4), 259-267, 2002. doi:10.1007/s00158-002-0188-0 5 V.K. Koumousis, C.P. Katsaras "A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance", IEEE Transactions on Evolutionary Computation, 10, 19-28, 2006. doi:10.1109/TEVC.2005.860765 6 C.K. Dimou, V.K. Koumousis, "Reliability based optimal design of truss structures using particle swarm optimization", Journal of Computing in Civil Engineering, ASCE, 23, 100-109, 2009. doi:10.1061/(ASCE)0887-3801(2009)23:2(100) purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £70 +P&P)
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