Parameter Tuning Strategies for Metaheuristic Methods Applied to Discrete Optimization of Structural Design
This work addresses parameter tuning challenges for structural engineers using metaheuristics, but it is incremental as it builds on existing methods with domain-specific adaptations.
The paper tackles the problem of tuning metaheuristic parameters for computationally expensive discrete optimization in reinforced concrete structural design, proposing a novel utility metric and surrogate models, and finds that Biogeography-Based Optimization outperforms GA and PSO in this context.
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.