Real-Time Solution of Unsteady Inverse Heat Conduction Problem Based on Parameter-Adaptive PID with Improved Whale Optimization Algorithm

Huang, Weichao; Li, Jiahao; Liu, Ding

doi:10.3390/en16010225

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…The performance of MSWOA needs to be assessed in real-world applications. Huang et al [ 162 ] proposed an improved whale optimization algorithm to adapt the parameter-adaptive proportional-integral-differential (PID). The optimal PID parameters are obtained using the IWOA algorithm.…”

Section: Improved Woa Variantsmentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

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Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

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Section: Improved Woa Variantsmentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

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“…However, the introduction of reinforcement learning techniques requires an appropriate dataset or training process to train the model, which may require additional data collection and processing efforts. Reference [18] optimized the initial population distribution of the algorithm; introduced segmented control parameters and adaptive weights to improve the convergence speed of the algorithm; added an adaptive learning factor to control the variability of each individual's learning ability to improve the algorithm's global searching ability; and introduced a Cauchy perturbation on the optimal individual, which avoids the problem that the algorithm is easy to fall into the local optimum. However, the above strategies emphasize global search, which may lead to premature convergence of the algorithm to a non-global optimal solution.…”

Section: Introductionmentioning

confidence: 99%

PI Parameters Tuning for Frequency Tracking Control of Wireless Power Transfer System Based on Improved Whale Optimization Algorithm

Yang,

Guan

2024

IEEE Access

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For the efficiency optimization problem of wireless power transfer system, the most important thing is that the frequency tracking control is needed to make the system work in a resonant state. The parameters of the frequency tracking controller directly determine the control performance. In this paper, we consider using an improved whale optimization algorithm to complete the PI parameter tuning of the frequency tracking controller as a way to save personnel's effort and time on the controller design. This improved whale optimization algorithm mainly has the following improvements: firstly, it uses Kent chaotic mapping to initialize the whale population, which improves the variegation of the initial solution; secondly, it introduces adaptive weight coefficients and nonlinearly improves the convergence factor, which achieves a balance between the algorithm's global and local searching ability; finally, it introduces sine-cosine algorithmic strategy and Cauchy reverse learning strategy, which avoids the problem that the algorithm is easy to mature prematurely. The superiority of the improved algorithm is verified through the testing of eight benchmark functions and the Wilcoxon rank sum test method. Finally, the algorithm is applied to the PI parameter optimization of frequency tracking control, and the phase difference curves under the control of different PI parameters are compared by simulation. The results show that the improved algorithm proposed in this paper has a better performance for the PI parameter tuning of frequency tracking control in wireless power transfer system, which can effectively help reduce the investment of human resources.

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“…Compared with the whole-domain algorithms, the sequential algorithms can perform real-time inversion of parameters at the current moment without information from the entire time domain, thus requiring less computational power. Numerous sequential algorithms have been proposed by researchers, including the sequential function specification method (SFSM) [23], digital filter method [24], Kalman filter algorithm [10,11], and proportional integral derivative (PID) algorithm [25,26]. Back [23] proposed the SFSM for the estimation of unknown parameters at the current time based on the temperature measurements over a future period.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results demonstrate that the PID algorithm has a good anti-disturbance ability. Huang [26] developed a real-time solution for unsteady IHCP based on the PID algorithm that enhanced by the improved whale optimization algorithm. Significant progress has been achieved in solving inverse heat conduction problems using sequential algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network-Based Method for Real-Time Inversion of Nonlinear Heat Transfer Problems

Chen,

Pan

2023

Energies

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Inverse heat transfer problems are important in numerous scientific research and engineering applications. This paper proposes a network-based method utilizing the nonlinear autoregressive with exogenous inputs (NARX) neural network, which can achieve real-time identification of thermal boundary conditions for nonlinear transient heat transfer processes. With the introduction of the NARX neural network, the proposed method offers two key advantages: (1) The proposed method can obtain inversion results using only surface temperature time series. (2) The heat flux can be estimated even when the state equation of the system is unknown. The NARX neural network is trained using the Bayesian regularization algorithm with a dataset comprising exact surface temperature and heat flux data. The neural network takes current and historical surface temperature measurements as inputs to calculate the heat flux at the current time. The capability of the NARX method has been verified through numerical simulation experiments. Experimental results demonstrate that the NARX method has high precision, strong noise resistance, and broad applicability. The composition of the input data, the surface temperature measurement noise, and the boundary heat flux shape have been studied in detail for their impact on the inversion results. The NARX method is a highly competitive solution to inverse heat transfer problems.

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Real-Time Solution of Unsteady Inverse Heat Conduction Problem Based on Parameter-Adaptive PID with Improved Whale Optimization Algorithm

Cited by 4 publications

References 22 publications

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

PI Parameters Tuning for Frequency Tracking Control of Wireless Power Transfer System Based on Improved Whale Optimization Algorithm

A Neural Network-Based Method for Real-Time Inversion of Nonlinear Heat Transfer Problems

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