Decoupling Optimization for Complex PDN Structures Using Deep Reinforcement Learning

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

“…1) Sequential Search Method [6]: This method selects decaps sequentially, and a GA is adopted to determine the location and type of each newly added decap. This method can significantly improve the optimization speed but sacrifices the solution quality [14]. Here, we replace the GA with a full search to determine the location and type for each newly added decap.…”

“…Metaheuristic searching algorithms such as genetic algorithm (GA) [4], [5], [6], [7], [8], [9] and particle swarm optimization (PSO) [10] have been proposed for decap optimization and demonstrate good performance in finding solutions with the minimum number of decaps. 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.…”

“…However, decap optimization is a combinatorial optimization problem with a large search space. In addition, different decap locations are not independent, and the collaborative contribution of different decaps should be considered, making it impossible to optimize each decap individually [14]. Therefore, it is a great challenge to efficiently find the optimal decap solution and well balance solution quality (i.e., minimizing the number of decaps in this article) and optimization efficiency in the huge search space.…”

“…Our code is open-source and available at https://github.com/SoFoolish250/ Physics-Assisted-Genetic-Algorithm-PAGA-for-Decap-Optimization. [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.…”

A Novel Physics-Assisted Genetic Algorithm(PAGA) for Decoupling Capacitor Optimization

“…Metaheuristic searching algorithms such as genetic algorithm (GA) [4], [5], [6], [7], [8], [9] and particle swarm optimization (PSO) [10] have been proposed for decap optimization and demonstrate good performance in finding solutions with the minimum number of decaps. 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.…”