A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. Here, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.

提出了一种新颖的设计优化方法（ActivO），该方法采用了一组机器学习算法。所提出的方法是基于代理的方案，其中在主动学习循环中利用了弱学习者和强学习者的预测。弱学习者用于确定要探索的设计空间内的有希望区域，而强学习者用于确定有希望区域内最优对象的确切位置。对于每个设计迭代，通过在学习者预测的适应性差的高区域内随机选择评估点来进行探索。一旦确定了最有前途的区域，也将评估通过使用强大的学习者作为替代者而获得的全局最优值，以实现快速收敛。第一，在余弦混合函数具有25个局部最优值和一个全局最优值的情况下，将ActivO的性能与其他五个优化器进行了比较。在第二个问题中，目标是在遵守与缸内压力和排放相关的期望约束的同时，将压燃内燃（IC）发动机的指示比燃料消耗降至最低。在这里，将提出的方法的功效与遗传算法的功效进行了比较，后者在内燃机领域广泛用于引擎优化，这表明ActivO减少了达到全局最优所需的功能评估次数。 -设计达到80％。此外，优化发动机设计参数还可以节省约1.9％的能耗，