Multimodal optimization: an effective framework for model calibration Chica Serrano, Manuel Barranquero, José Kadjanowicz, Tomasz Damas Arroyo, Sergio Cordón García, Óscar Model calibration Multimodal optimization Genetic algorithms This work is supported by Spanish Ministerio de Economía y Competitividad under the NEWSOCO project (ref. TIN2015-67661), including European Regional Development Funds (ERDF), statutory activities of the Faculty of Computer Science and Management of Wroclaw University of Technology and by the European Commission under the 7th Framework Programme, Coordination and Support Action, Grant Agreement Number 316097, Engine project. Automated calibration is a crucial stage when validating non-linear dynamic systems. The modeler must control the calibration results and analyze parameter values in an iterative way. In many non-linear models, it is usual to find sets of configuration parameters that may obtain the same model fitting. In these cases, the modeler needs to understand the results’ implications and run a sensitivity analysis to check the model validity. This paper presents a framework based on niching genetic algorithms to provide modeler with a set of alternative calibration solutions which also ease the analysis of their parameters, model’s response, and sensitivity analysis. The framework is called MOMCA, an integral and interactive solution for model validation which facilitates the implication of decision makers. The core component of MOMCA is its niching genetic algorithm, able to reach various optima in multimodal optimization problems by keeping the necessary diversity. The proposed framework is applied to two different case studies. The first case study is a biological growth model and the second one is a managerial model to improve brand equity. Both applications show the benefits of the framework when providing a set of calibrated models and a way to analyze and perform sensitivity analysis based on the set of solutions. 2025-06-24T07:38:48Z 2025-06-24T07:38:48Z 2017-01-01 journal article Chica Serrano, Manuel et al. Information Sciences 375, 79-97. https://doi.org/10.1016/j.ins.2016.09.048 https://hdl.handle.net/10481/104795 10.1016/j.ins.2016.09.048 eng info:eu-repo/grantAgreement/EC/FP7/316097 open access Elsevier