Empirical analysis of memetic algorithms for conceptual design of steel structural systems in tall buildings

Abstract

This paper discusses the results of extensive design experiments in which memetic algorithms were applied to optimize topologies of steel structural systems in tall buildings. In these experiments. evolutionary algorithms were employed to determine optimal configurations of structural members (topology optimization) while the optimal cross-sections of members (sizing optimization) were found using continuous/discrete optimization algorithm implemented in SODA. The impact of all major evolutionary computation parameters on the performance of memetic algorithms was investigated. Two classes of complex structural design problems were considered: design of a wind bracing system in a tall building and design of the entire steel structural system in a tall building. The total weight of the structural system was assumed as the optimality criterion with respect to which the designs were optimized while satisfying all design requirements specified by appropriate design codes. In the conducted experiments various key EC parameters and their values were considered having the largest impact on the performance of memetic algorithms. It was discovered that the type of EA. the rate of mutation operator, and the size of parent population were critical for the success of structural optimization processes. Specifically, evolution strategies produced on average significantly better results than genetic algorithms for the design problems considered in the paper. Also, low mutation rates, i.e. 0.025, resulted in best performance of memetic algorithms. Furthermore, small parent population sizes were generally preferred to large populations. For the simpler problem of conceptual design of a wind bracing system, optimal results were produced even when the population with a single member was used. In the case of the second and more complex design problem slightly larger population sizes were required consisting of 5 members. Results of a large number of design experiments allowed formulating initial recommendations regarding optimal parameter settings for memetic algorithms for structural design applications. The experiments also produced a body of structural design knowledge, both quantitative and qualitative in nature. They identified regions of the design spaces in which high-performance solutions can be found. They also defined the ranges of the total weight of structural systems associated with high-performance solutions for both classes of design problems. Furthermore, significant qualitative differences between high-performance solutions have been identified. The structural shaping patterns exhibited by high-performing designs ranged from crossed macrodiagonal patterns composed of X bracings to irregular patterns consisting of various types of bracings. © 2006 Springer.