A surrogate merit function developed for structural weight optimization problems

Springer Science and Business Media LLC
Publication Type:
Journal Article
Soft Computing, 2022, pp. 1-31
Issue Date:
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In this paper, a surrogate merit function (SMF) is proposed to be evaluated instead of the traditional merit functions (i.e., penalized weight of the structure). The standard format of the conventional merit functions needs several expensive trial-and-error tuning processes to enhance optimization convergence quality, retuning for different structural model configurations, and final manual local search in case optimization converges to infeasible vicinity of global optimum. However, on the other hand, SMF has no tunning factor but shows statistically stable performance for different models, converges directly to outstanding feasible points, and shows other superior advantages such as reduced required iterations to achieve convergence. In other words, this new function is a no-hassle one due to its brilliant user-friendly application and robust numerical results. SMF might be a revolutionary step in commercializing design optimization in the real-world construction market.
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