  This paper presents several strategies to tune the parameters of
metaheuristic methods for (discrete) design optimization of reinforced concrete
(RC) structures. A novel utility metric is proposed, based on the area under
the average performance curve. The process of modelling, analysis and design of
realistic RC structures leads to objective functions for which the evaluation
is computationally very expensive. To avoid costly simulations, two types of
surrogate models are used. The first one consists of the creation of a database
containing all possible solutions. The second one uses benchmark functions to
create a discrete sub-space of them, simulating the main features of realistic
problems. Parameter tuning of four metaheuristics is performed based on two
strategies. The main difference between them is the parameter control
established to perform partial assessments. The simplest strategy is suitable
to tune good `generalist' methods, i.e., methods with good performance
regardless the parameter configuration. The other one is more expensive, but is
well suited to assess any method. Tuning results prove that Biogeography-Based
Optimization, a relatively new evolutionary algorithm, outperforms other
methods such as GA or PSO for such optimization problems, due to its particular
approach of applying recombination and mutation operators.
