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.

校准基于代理的模型涉及估计多个参数值。可以使用自动校准自动执行此操作，但其成功取决于优化方法探索参数搜索空间的能力。本文建议使用珊瑚礁优化算法来执行此过程，这是竞争生物启发式元启发法的一个新分支，除了其新颖的隐喻外，它还显示出在其他优化问题中的良好行为。这些针对模型校准的元启发式方法的性能是通过针对完善的和最新的进化算法（包括它们与本地搜索程序的混合）进行详尽的实验来评估的。这项研究使用整数编码方案对基于代理的基于模型的12个问题实例的基准进行了整数编码方案分析，该校准精度的决策变量数量不断增加。对结果进行统计测试后，报告了模因性珊瑚礁优化的出色性能。