Multiport PDN Optimization With the Newton–Hessian Minimization Method

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

“…3) Newton-Hessian Method [15]: The Newton-Hessian method optimizes the decap locations as continuous variables. In this method, the decaps are also added sequentially.…”

“…Decaps Number GA in [7] 1266 79 GA in [8] 887 43 GA in [9] 187 24 Sequential search method [6] \ 61 Sorting search method [16] \ No Sol. Newton-Hessian method [15] \ 101 Proposed PAGA 61 24 The convergence process of the proposed PAGA and other GA methods is compared in Fig. 3.…”

“…The consumed time and the number of decaps of different methods are listed in Table II. As mentioned earlier, the sequential search [6] and Newton-Hessian [15] methods cannot find the optimal solution because they optimize different decaps individually rather than collaboratively. The sorting search method [16] cannot find a feasible solution even after a long search.…”

“…Recently, with the popularization of artificial intelligence, machine learning (ML)-based methods, such as reinforcement learning (RL) [11], [12], [13], [14], have been broadly adopted in PI optimization problems. Besides, some algorithms based on human experience and knowledge have also been proposed to quickly determine the decap distribution, such as the Newton-Hessian minimization method [15] and several other approaches [16], [17], [18], [19], [20] with different empirical knowledge and decision-making rules.…”

“…[11], [12], [13], [14] require a significant amount of time for data generation and model training, and the robustness and generalization performance of the RL models are difficult to ensure. The human-knowledge-inspired methods [15], [16], [17], [18], [19], [20] can find feasible decap solutions, but the solution quality cannot be guaranteed for large-scale scenarios. Besides, some methods mentioned above [13], [20] can only optimize either the selection of decap types or the decap locations, but not simultaneously.…”