Abstract
Complex problems can be analyzed by using model simulation but its use is not straight-forward since modelers must carefully calibrate and validate their models before using them. This is specially relevant for models considering multiple outputs as its calibration requires handling different criteria jointly. This can be achieved using automated calibration and evolutionary multiobjective optimization methods which are the state of the art in multiobjective optimization as they can find a set of representative Pareto solutions under these restrictions and in a single run. However, selecting the best algorithm for performing automated calibration can be overwhelming. We propose to deal with this issue by conducting an exhaustive analysis of the performance of several evolutionary multiobjective optimization algorithms when calibrating several instances of an agent-based model for marketing with multiple outputs. We analyze the calibration results using multiobjective performance indicators and attainment surfaces, including a statistical test for studying the significance of the indicator values, and benchmarking their performance with respect to a classical mathematical method. The results of our experimentation reflect that those algorithms based on decomposition perform significantly better than the remaining methods in most instances. Besides, we also identify how different properties of the problem instances (i.e., the shape of the feasible region, the shape of the Pareto front, and the increased dimensionality) erode the behavior of the algorithms to different degrees.
Highlights
Model simulation is a common approach to the analysis of complex phenomena
These results are again consistent with the previous indicator values, as multiobjective evolutionary algorithm based on decomposition (MOEA/D) shows an outstanding and robust behavior, being able to perform significantly better than the remaining algorithms in most instances
In the case of P6, SMS-EMOA and non-dominated sorting genetic algorithm II (NSGA-II) are the best performing algorithms and significantly outperform MOEA/D
Summary
Model simulation is a common approach to the analysis of complex phenomena. It allows users and stakeholders to recreate the desired dynamics so these phenomena could be studied in a controlled environment, where different policies or strategies can be tested. VOLUME 9, 2021 model for our experiments This ABM tackles marketing scenarios involving two conflicting outputs or key performance indicators: the global awareness of the consumers regarding the brands available in the market and the number of word-of-mouth consumer interactions for those brands. Both the instances and historical data for our calibration benchmark are taken from a real banking marketing scenario in Spain. There is previous work using EMO for multicriteria calibration of ABMs [35]–[37], none of these contributions considers a rigorous and exhaustive comparison of several EMO algorithms for calibrating multiple model instances and the subsequent analysis of the algorithms’ performance according to the problem characteristics.
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