An enhanced grey wolf optimizer with fusion strategies for identifying the parameters of photovoltaic models

Luo, Jinkun; He, Fazhi; Gao, Xiaoxin

doi:10.3233/ica-220693

Cited by 21 publications

(3 citation statements)

References 99 publications

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“…To the best of our knowledge, this research represents the inaugural exploration of meta-heuristic optimization technique for the fine-tuning of GAN, specifically guided by the FID score. Future research could potentially adopt more exhaustive optimization methods [44] , [45] and experimentation strategies to determine optimal hyperparameters. While the primary aim of this study was to identify an effective GAN for generating realistic DME images, DME detection was performed solely using the widely adopted ResNet50 model.…”

Section: Discussionmentioning

confidence: 99%

Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs)

Tripathi¹,

Kumar²,

Mayya³

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs)

Tripathi¹,

Kumar²,

Mayya³

et al. 2023

Heliyon

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“…After that, many efcient evolutionary algorithms are designed, and they can be grouped in the three main categories which include swarm-based, evolution-based, and physics-based methods. In detail, swarm-based methods [38,[57][58][59] mimic the cooperative mechanism of group animals. Evolution-based methods [60][61][62][63][64][65][66] are inspired by Darwinian evolution law, which follows the saying: survival of the fttest and elimination of the unft.…”

Section: Evolutionary Algorithmmentioning

confidence: 99%

Explore Gap between 3D DNN and Human Vision Utilizing Fooling Point Cloud Generated by MEHHO

Fan

et al. 2023

Security and Communication Networks

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Deep neural network (DNN) has replaced humans to make decisions in many security-critical senses such as face recognition and automatic drive. Essentially, researchers try to teach DNN to simulate human behavior. However, many evidences show that there is a huge gap between humans and DNN, which has raised lots of security concern. Adversarial sample is a common way to show the gap between DNN and humans in recognizing objects with similar appearance. However, we argue that the difference is not limited to adversarial samples. Hence, this paper explores such differences in a new way by generating fooling samples in 3D point cloud domain. Specifically, the fooling point cloud is hardly recognized by human vision but is classified to the target class by the victim 3D point cloud DNN (3D DNN) with more than 99.99 % confidence. Furthermore, to search for the optimal fooling point cloud, a new evolutionary algorithm named Multielites Harris Hawk Optimization (MEHHO) with enhanced exploitation ability is designed. On one hand, our experiments demonstrate that: (1) 3D DNN tends to learn high-level features of one object; (2) 3D DNN that makes decisions relying on more points is more robust; and (3) the gap is hardly learned by 3D DNN. On the other hand, the comparison experiments show that the designed MEHHO outperforms the SOTA evolutionary algorithms w.r.t. statistics and convergence results.

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“…The top level only needs to focus on which loop needs to be adjusted most, while the low level only needs to focus on the specific operation of the loop. It is the cooperation between global exploration and local exploitation, that can significantly improve accuracy and reliability [75,76].…”

Section: Hierarchical Options Frameworkmentioning

confidence: 99%

3D reconstruction based on hierarchical reinforcement learning with transferability

Fan

et al. 2023

ICA

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3D reconstruction is extremely important in CAD (computer-aided design)/CAE (computer-aided Engineering)/CAM (computer-aided manufacturing). For interpretability, reinforcement learning (RL) is used to reconstruct 3D shapes from images by a series of editing actions. However, typical applications of RL for 3D reconstruction face problems. The search space will increase exponentially with the action space due to the curse of dimensionality, which leads to low performance, especially for complex action spaces in 3D reconstruction. Additionally, most works involve training a specific agent for each shape class without learning related experiences from others. Therefore, we present a hierarchical RL approach with transferability to reconstruct 3D shapes (HRLT3D). First, actions are grouped into macro actions that can be chosen by the top-agent. Second, the task is accordingly decomposed into hierarchically simplified sub-tasks solved by sub-agents. Different from classical hierarchical RL (HRL), we propose a sub-agent based on augmented state space (ASS-Sub-Agent) to replace a set of sub-agents, which can speed up the training process due to shared learning and having fewer parameters. Furthermore, the ASS-Sub-Agent is more easily transferred to data of other classes due to the augmented diverse states and the simplified tasks. The experimental results on typical public dataset show that the proposed HRLT3D performs overwhelmingly better than recent baselines. More impressingly, the experiments also demonstrate the extreme transferability of our approach among data of different classes.

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An enhanced grey wolf optimizer with fusion strategies for identifying the parameters of photovoltaic models

Cited by 21 publications

References 99 publications

Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs)

Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs)

Explore Gap between 3D DNN and Human Vision Utilizing Fooling Point Cloud Generated by MEHHO

3D reconstruction based on hierarchical reinforcement learning with transferability

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