Computer Science – Neural and Evolutionary Computing
Scientific paper
2009-02-10
Computers & Structures, 81 (18-19), 1979-1990, 2003
Computer Science
Neural and Evolutionary Computing
25 pages, 8 figures, 5 tables
Scientific paper
10.1016/S0045-7949(03)00217-7
This paper presents comparison of several stochastic optimization algorithms developed by authors in their previous works for the solution of some problems arising in Civil Engineering. The introduced optimization methods are: the integer augmented simulated annealing (IASA), the real-coded augmented simulated annealing (RASA), the differential evolution (DE) in its original fashion developed by R. Storn and K. Price and simplified real-coded differential genetic algorithm (SADE). Each of these methods was developed for some specific optimization problem; namely the Chebychev trial polynomial problem, the so called type 0 function and two engineering problems - the reinforced concrete beam layout and the periodic unit cell problem respectively. Detailed and extensive numerical tests were performed to examine the stability and efficiency of proposed algorithms. The results of our experiments suggest that the performance and robustness of RASA, IASA and SADE methods are comparable, while the DE algorithm performs slightly worse. This fact together with a small number of internal parameters promotes the SADE method as the most robust for practical use.
Hrstka O.
Kucerova Anna
Leps M.
Zeman Jan
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