The neural-network (NN)-based control method is a new emerging promising technique for controller design in a power electronic circuit (PEC). However, the optimization of NN-based controllers (NNCs) has significant challenges in two aspects. The first challenge is that the search space of the NNC optimization problem is such complex that the global optimization ability of the existing algorithms still needs to be improved. The second challenge is that the training process of the NNC parameters is very computationally expensive and requires a long execution time. Thus, in this article, we develop a powerful evolutionary computation-based algorithm to find a high-quality solution and reduce computational time. First, the differential evolution (DE) algorithm is adopted because it is a powerful global optimizer in solving a complex optimization problem. This can help to overcome the premature convergence in local optima to train the NNC parameters well. Second, to reduce the computational time, the DE is extended to distribute DE (DDE) by dispatching all the individuals to different distributed computing resources for parallel computing. Moreover, a resource-aware strategy (RAS) is designed to further efficiently utilize the resources by adaptively dispatching individuals to resources according to the real-time performance of the resources, which can simultaneously concern the computing ability and load state of each resource. Experimental results show that, compared with some other typical evolutionary algorithms, the proposed algorithm can get significantly better solutions within a shorter computational time.

中文翻译：

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用于训练电力电子电路中昂贵的基于神经网络的控制器的资源感知分布式差分进化

基于神经网络 (NN) 的控制方法是一种新兴的有前途的电力电子电路 (PEC) 控制器设计技术。然而，基于 NN 的控制器 (NNC) 的优化在两个方面存在重大挑战。第一个挑战是NNC优化问题的搜索空间非常复杂，现有算法的全局优化能力仍有待提高。第二个挑战是 NNC 参数的训练过程计算量非常大，并且需要很长的执行时间。因此，在本文中，我们开发了一种强大的基于进化计算的算法，以找到高质量的解决方案并减少计算时间。第一的，采用差分进化（DE）算法是因为它是解决复杂优化问题的强大全局优化器。这有助于克服局部最优的过早收敛，从而很好地训练 NNC 参数。其次，为了减少计算时间，DE被扩展为分布式DE（DDE），通过将所有个体分配到不同的分布式计算资源进行并行计算。此外，还设计了一种资源感知策略（RAS），通过根据资源的实时性能自适应地将个体调度到资源中来进一步有效地利用资源，该策略可以同时关注每个资源的计算能力和负载状态。实验结果表明，与其他一些典型的进化算法相比，