In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

Artificial Neural Networks Based Optimization Techniques: A Review

The current demand in Power Distribution Network (PDN) design is characterized by the accurate placement of decoupling capacitors and the minimization of their number aimed at cost saving. The paper proposes an optimization algorithm for accordingly placing decoupling capacitors one-by-one and iteratively evaluating the cost function of each PDN design solution. This allows the designer to identify the minimum number of decaps whenever the input impedance satisfies the target impedance requirements. The algorithm is based on the Genetic Algorithm accordingly adapted for the specific application of PDN design. It may involve the evaluation of the input impedance at multiple locations, representing either multiple ICs, as well as multiple power input areas/pins of the same IC. The validation of the developed optimization algorithm is carried out by applying it to a manufactured PCB and by employing typical (low inductance) decaps for PDN design. The optimization process led to a decap configuration that effectively takes into account the decap value, the parasitics inductance, and the decap location. An accurate experimental test further validates the optimized PDN.

Genetic Algorithm PDN Optimization based on Minimum Number of Decoupling Capacitors Applied to Arbitrary Target Impedance

An iterative optimization for decoupling capacitor placement on a power delivery network (PDN) is presented based on Genetic Algorithm (GA) and Artificial Neural Network (ANN). The ANN is first trained by an appropriate set of results obtained by a commercial simulator. Once the ANN is ready, it is used within an iterative GA process to place a minimum number of decoupling capacitors for minimizing the differences between the input impedance at one or more location, and the required target impedance. The combined GA–ANN process is shown to effectively provide results consistent with those obtained by a longer optimization based on commercial simulators. With the new approach the accuracy of the results remains at the same level, but the computational time is reduced by at least 30 times. Two test cases have been considered for validating the proposed approach, with the second one also being compared by experimental measurements.

https://www.mdpi.com/2079-9292/9/8/1243/pdf

Effective PCB Decoupling Optimization by Combining an Iterative Genetic Algorithm and Machine Learning

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Pcb Design For Real World Emi Control

This article proposes a jitter-aware decoupling capacitors placement optimization method that uses the genetic algorithm (GA). A novel method for defining the optimization target function in regard to power delivery network (PDN) and power source-induced jitter (PSIJ) optimization based on the GA-based tool is proposed. The proposed method can provide an optimum and economic solution for the number of decoupling capacitors to use in a PDN to reach the target impedance. Then, by modifying the optimization target function with our proposed method, an optimum solution of the number of decoupling capacitors regarding the PSIJ can be obtained. The PSIJ analytical expressions are derived in conjunction with a resonant cavity model that includes the coordinates of the decoupling capacitors and the PSIJ transfer function. The GA-based optimization algorithm with the proposed target function is first applied to optimize the number of decoupling capacitors regarding the PSIJ. Finally, the measured jitters from HSPICE simulation results are used to verify our optimization method such that both the simulated results and analytically calculated results support the efficiency of our proposed optimization method.

/pdf/jitter-aware-economic-pdn-optimization-with-a-genetic-sgbt28aset.pdf

Jitter-Aware Economic PDN Optimization With a Genetic Algorithm

Decoupling capacitors are fundamental keys for the reduction of transient noise in power delivery networks; their arrangement and values are crucial for reaching this goal. This work deals with the optimization of the decoupling capacitors of a power delivery network by using a nature-inspired algorithm. In particular, the capacitance value and the location of three decoupling capacitors are optimized in order to obtain an input impedance below a specific mask, by using a nature-inspired algorithm, the genetic one, in combination with two electromagnetic solvers used to compute the objective function. An experimental board is designed and manufactured; measurements are performed to validate the numerical results.

https://www.mdpi.com/2079-9292/8/7/737/pdf

Decoupling Capacitors Placement at Board Level Adopting a Nature-Inspired Algorithm

To reduce the noise created by a power delivery network, the number, the value of decoupling capacitors and their arrangement on the board are critical to reaching this goal. This work deals with specific improvements, implemented on a genetic algorithm, which used for the optimization of the decoupling capacitors in order to obtain the frequency spectrum of the input impedance in different positions on the network, below previously defined values. Measurements are performed on a specifically manufactured board in order to validate the effectiveness of the proposed algorithm and the optimization results obtained for a specific example board.

/pdf/efficient-iterative-process-based-on-an-improved-genetic-ayr4ntpvgu.pdf

Markus Buecker

Papers

Genetic Algorithm PDN Optimization based on Minimum Number of Decoupling Capacitors Applied to Arbitrary Target Impedance

Effective PCB Decoupling Optimization by Combining an Iterative Genetic Algorithm and Machine Learning

Decoupling Capacitors Placement at Board Level Adopting a Nature-Inspired Algorithm

Efficient Iterative Process Based on an Improved Genetic Algorithm for Decoupling Capacitor Placement at Board Level