An Improved Lion Swarm Optimization Algorithm Based on Tent-map and Differential Evolution

Liu, Miaomiao; Zhang, Yuying; Yao, Dan; Guo, Jingfeng; Chen, Jing

doi:10.1109/ccet55412.2022.9906355

Cited by 3 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, the algorithm still tended to get trapped in local optimal solutions, indicating that its global search capability remains to be improved. Liu et al [18] proposed an adaptive lion swarm optimization algorithm incorporating chaotic search and information entropy to solve the problem that LSO algorithm has a slow convergence speed and tends to fall into the local optimal in subsequent iterations. The accuracy and stability of the algorithm were proved to be excellent, but its advantages were not extended to multi-objective problems.…”

Section: Introductionmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

View full text Add to dashboard Cite

Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

show abstract

Section: Introductionmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

View full text Add to dashboard Cite

show abstract

“…In the SSA initialization stage, due to the random generation of the initial position of the sparrows, there is a high probability that the distribution of individual sparrows in the population is uneven, which reduces the diversity of the population and slows down the optimization rate. The positive effect, randomness, ergodicity, periodicity, and initial value sensitivity of chaos are used to improve the optimization algorithm in many optimization methods that use chaotic maps as random number generators and introduce them into SSA [28]. There are two common types of chaotic maps, namely tent chaotic map and logical chaotic map.…”

Section: Improved Sparrow Search Algorithm a Tent Chaotic Mapmentioning

confidence: 99%

Fault Diagnosis of Ship Ballast Water System Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm

Wang,

Cao,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The key to reducing operation and maintenance costs and improving reliability is to evaluate the condition and fault detection methods of ship ballast water systems. To reduce the impact of support vector machine (SVM) parameter uncertainty and improve the accuracy of ship ballast water system fault diagnosis models, this paper proposes a multi-strategy collaborative improved sparrow search algorithm (ISSA) to optimize the parameters of SVM for fault diagnosis. First, ISSA initializes the population using tent chaotic maps to improve spatial distribution uniformity and stabilize the population quality. Second, the discoverer in the algorithm adopts an adaptive weighting strategy to avoid falling into local optima and accelerate the convergence speed. Finally, the follower position introduces mutation strategies and chaotic perturbations to enhance local evolution capabilities and improve the utilization of search areas. The experimental results show that compared with sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), Genetic Algorithm (GA), whale optimization algorithm (WOA), and grey wolf optimization algorithm (GWO), ISSA can obtain better optimal solutions at the fastest convergence rate on the benchmark test function. On the dataset of the ship ballast water system, the ISSA-SVM fault diagnosis model proposed in this paper has the best classification performance, with an average accuracy of 97.44%. Compared with the other two models, ISSA-SVM is 7.55% and 0.83% higher than SVM's 89.89% and SSA-SVM's 96.61%.

show abstract