In the traffic light scheduling problem, the evaluation of candidate solutions requires the simulation of a process under various (traffic) scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has shown that combining IRACE with evolutionary operators is effective for this task due to the power of evolutionary operators in numerical optimization. In this article, we further explore the hybridization of evolutionary operators and the elitist iterated racing of IRACE for the simulation–optimization of traffic light programs. We review previous works from the literature to find the evolutionary operators performing the best when facing this problem to propose new hybrid algorithms. We evaluate our approach over a realistic case study derived from the traffic network of Málaga (Spain) with 275 traffic lights that should be scheduled optimally. The experimental analysis reveals that the hybrid algorithm comprising IRACE plus differential evolution offers statistically better results than the other algorithms when the budget of simulations is low. In contrast, IRACE performs better than the hybrids for a high simulations budget, although the optimization time is much longer.

在交通灯调度问题中，候选解决方案的评估需要在各种（交通）场景下模拟一个过程。因此，好的解决方案不仅应该实现良好的目标函数值，而且它们必须在所有不同场景中都是稳健的（低方差）。先前的工作表明，由于进化算子在数值优化中的强大功能，将 IRACE 与进化算子相结合对于这项任务是有效的。在这篇文章中，我们进一步探索了进化算子的混合和 IRACE 的精英迭代竞赛，以模拟优化交通灯程序。我们从文献中回顾了以前的工作，以找到在面对这个问题时表现最好的进化算子，以提出新的混合算法。我们通过来自马拉加（西班牙）交通网络的真实案例研究来评估我们的方法，其中有 275 个交通信号灯应该被优化安排。实验分析表明，当模拟预算较低时，由 IRACE 和差分进化组成的混合算法在统计上比其他算法提供了更好的结果。相比之下，IRACE 在高模拟预算方面比混合动力车表现更好，尽管优化时间要长得多。