Coral reefs optimization algorithms for agent-based model calibration

Calibrating agent-based models involves estimating multiple parameter values. This can be performed automatically using automatic calibration but its success depends on the optimization method’s ability for exploring the parameter search space. This paper proposes to carry out this process using coral reefs optimization algorithms, a new branch of competitive bio-inspired metaheuristics that, beyond its novel metaphor, has shown its good behavior in other optimization problems. The performance of these metaheuristics for model calibration is evaluated by conducting an exhaustive experimentation against well-established and recent evolutionary algorithms, including their hybridization with local search procedures. The study analyzes the calibration accuracy of the metaheuristics using an integer coding scheme over a benchmark of 12 problem instances of an agent-based model with an increasing number of decision variables. The outstanding performance of the memetic coral reefs optimization is reported after performing statistical tests to the results.