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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 99
Edited by: B.H.V. Topping
Paper 68

A Comparison of Genetic Algorithm and Particle Swarm Optimisation for Theoretical and Structural Applications

Z. Wang, T.J. McCarthy and M.N. Sheikh

Faculty of Engineering, University of Wollongong, Australia

Full Bibliographic Reference for this paper
Z. Wang, T.J. McCarthy, M.N. Sheikh, "A Comparison of Genetic Algorithm and Particle Swarm Optimisation for Theoretical and Structural Applications", in B.H.V. Topping, (Editor), "Proceedings of the Eleventh International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 68, 2012. doi:10.4203/ccp.99.68
Keywords: optimisation, evolution, genetic algorithm, particle swarm optimisation, efficiency, performance comparison, population size, truss.

Genetic algorithms (GAs) and particle swarm optimisation (PSO) are well-known for their ability in obtaining global optima. Some evidence exists in the structural engineering literature that PSO involves less overall computation effort than a GA. Hence, these two methods have been selected and benchmarked against each other to test their relative robustness and efficiency for structural optimisation applications. This paper examines the performance and efficiency of these two optimisation algorithms in solving both mathematical benchmark functions and the classical ten-bar truss redundant problem. The mathematical functions used are a ten-dimensional Rosenbrock valley function and a ten-dimensional Schwefel function. These have many local minima and each has a unique and known global minimum. Tests are performed to assess the performance of each in relation to the population size required and the number of generations to achieve convergence. The GA produces slightly better performance for Rosenbrock's function while the PSO works best for Schwefel's function. Both methods find the correct solutions when enough iterations are allowed. The PSO performs best when a limited population or limited iterations are used. When the ten-bar redundant truss problem is considered, the PSO clearly outperforms the GA for all categories tested. To examine the efficiency of these two algorithms, population sizes of 10, 50, 100, 150 and 200 were used. The best results at iteration counts 100, 200, 400 and 800 were logged. Both methods are sensitive to the population size. The larger the number of candidates in the population, the better the solution for both the GA and PSO. PSO seems to perform better than the GA for smaller populations. Therefore, for the more complex problems, the PSO is shown to outperform the GA for smaller population sizes and limited iterations.

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