Surrogate Models and Ensemble Strategies for Expensive Evolutionary Optimization: An Industrial Case Study

Expensive optimization problems are characterized by the significant amount of time and resources needed to determine the quality of potential solutions. This poses severe limitations for the application of metaheuristic optimization methods, such as evolutionary algorithms, as they usually require evaluating many candidate solutions to deliver satisfactory results. Surrogate model-based strategies have become a popular choice to tackle this type of problem. The key idea of these strategies is to build a model which can approximate and (partially) replace the mechanisms for assessing solution quality, such that the use of this less expensive alternative lowers the overall computational cost of the optimization process. This paper analyzes surrogate model-based strategies in the context of a specific, expensive, combinatorial optimization problem: the configuration of the gas distribution system for an electrostatic precipitator. Focusing on this relevant case study from industry, the aim of this paper is twofold: (i) to investigate the most suitable learning techniques for building the surrogate models and (ii) to explore the advantages of ensemble strategies allowing various surrogate models to collaborate during optimization. This contrasts with previous studies where a single, fixed modeling technique is adopted to address this problem. The experimental evaluation involves eight different learning techniques, three alternative ensemble strategies, and two reference approaches from the literature (previously used to tackle this specific problem). Our results reveal the best modeling techniques at the individual level, while highlighting clear benefits of the simultaneous exploitation of multiple surrogate models when facing this particular optimization challenge.