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Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems

IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1176-1193页

作者： Xiaoling Wang Qi Kang MengChu Zhou Qi Deng Zheng Fan Haoyue Liu Department of Control Science and Engineering Tongji University Shanghai China College of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

To realize Industry 5.0, manufacturers face various optimization problems that seldom appear in isolation. Evolutionary MultiTasking (EMT) is an effective method to solve multiple related problems by extracting and utilizing common knowledge. Knowledge transfer is the key to the effectiveness of EMT. Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance. There is plentiful knowledge in assistant tasks, and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks. EMT is thus confronted with a challenge to find appropriate knowledge. This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks. During the evolution process, better-performing candidates are supposed to have advantages in exploitation. Therefore, assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge. Specifically, the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population. Considering that target and assistant sub-populations have different characteristics, we use domain adaptation to reduce their distribution discrepancies. In this way, the trained classifier can classify assistant individuals more accurately, and truly useful knowledge can be selected for target tasks. The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems.

关键词： Learning systems Diversity reception Machine learning Learning (artificial intelligence) Multitasking Fifth Industrial Revolution Optimization Knowledge transfer Faces Convergence

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Complex Network Pinning control based on Adjacency Degree and Resistance Distance Algorithm 3

Complex Network Pinning Control based on Adjacency Degree an...

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3rd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2023

作者： Zhong, Hao Jia, Haoran Jia, Yanting Sun, Haiyi School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Office of Academic Affairs Shenyang Jianzhu University Shenyang China Department of Science Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9781510668867

In this paper, a new strategy for pinning control node selection is proposed, namely: pinning control strategy based on ADRD (Adjacency Degree and Resistance Distance) algorithm. Firstly, the model of the undirected network and the sufficient conditions to achieve synchronization are given. Secondly, the steps of the pinning control strategy based on the ADRD algorithm are specifically given and applied to dolphins network, and by comparing the pinning control strategy based on the resistance distance, it is concluded that: the pinning control strategy based on the ADRD algorithm is better than the pinning control strategy based on the resistance distance. Finally, the correctness and effectiveness of the method are verified by numerical simulation. © 2023 SPIE.

关键词： Complex networks

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Pinning control of Complex Networks Based on the entropy of Betweenness centrality and Node strength 3

Pinning Control of Complex Networks Based on the entropy of ...

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3rd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2023

作者： Sun, Haiyi Cai, Liang Lv, Chenxinxiao Jia, Yanting Department of Science Shenyang Jianzhu University Shenyang China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Office of academic affairs Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9781510668867

In order to solve the problem of pinning node selection in undirected weighted networks, a pinning control strategy based on the entropy of betweenness centrality and node strength is proposed in this paper. Firstly, a complex network model is given where each node is the same dynamical system, secondly, sufficient conditions for the synchronization of this network are given, and specific steps of the betweenness centrality and node strength entropy-based pinning control strategy are proposed and applied to different networks. Finally, by comparing the neighborhood information entropy-based pinning control strategy, it is found that in order to synchronize the network, the number of key nodes pinned by the proposed pinning control strategy is smaller than the neighborhood information entropy-based pinning control strategy under different coupling strengths. © 2023 SPIE.

关键词： Complex networks

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Enhancing Object Detection Robustness for Cross-Depiction Through Neural Style Transfer 8

Enhancing Object Detection Robustness for Cross-Depiction Th...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Fiani, Francesca Puglisi, Adriano Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Modern neural networks models for computer vision are trained on millions of images. The idea is that models are able to increase generalization when the dataset contains well diversified images, e.g. with varied illumination and environmental conditions of the same objects. Generalization is particularly relevant in object detection, especially for what concerns the cross-depiction problem. In this work we explore the use of Neural Style Transfer as a novel technique to morph the original data, with the aim to enhance model generalization. To verify the effect on performances for object detection models, we selected the Faster R-CNN model to be applied on the Pascal VOC 2012 dataset. A number of tests were performed through style variations on images and by tuning Neural Style Transfer parameters to maintain the content of the original images. The experiments showed promising results, which effectively provide a foundation for future studies on cross-depiction via Neural Style Transfer. © 2023 Copyright for this paper by its authors.

关键词： Object detection

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BigFeat: Scalable and Interpretable Automated Feature engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Eldeeb, Hassan Amashukeli, Shota Elshawi, Radwa University of Tartu Institute of Computer Science Tartu Estonia Tanta University Faculty of Engineering Computers and Automatic Control Department Tanta Egypt

ISBN: (纸本)9781665480451

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features' quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat. © 2022 IEEE.

关键词： Scalability

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Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

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science China(Information sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

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Truthful Matching with Online Items and Offline Agents 50

Truthful Matching with Online Items and Offline Agents

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50th International Colloquium on Automata, Languages, and Programming, ICALP 2023

作者： Feldman, Michal Fusco, Federico Mauras, Simon Reiffenhäuser, Rebecca Blavatnik School of Computer Science Tel Aviv University Israel Microsoft Research Herzliya Israel Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Italy Institute for Logic Language and Computation University of Amsterdam Netherlands

ISBN: (纸本)9783959772785

We study truthful mechanisms for welfare maximization in online bipartite matching. In our (multiparameter) setting, every buyer is associated with a (possibly private) desired set of items, and has a private value for being assigned an item in her desired set. Unlike most online matching settings, where agents arrive online, in our setting the items arrive online in an adversarial order while the buyers are present for the entire duration of the process. This poses a significant challenge to the design of truthful mechanisms, due to the ability of buyers to strategize over future rounds. We provide an almost full picture of the competitive ratios in different scenarios, including myopic vs. non-myopic agents, tardy vs. prompt payments, and private vs. public desired sets. Among other results, we identify the frontier up to which the celebrated e/(e − 1) competitive ratio for the vertex-weighted online matching of Karp, Vazirani and Vazirani extends to truthful agents and online items. © Michal Feldman, Federico Fusco, Simon Mauras, and Rebecca Reiffenhäuser.

关键词： Sales

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Fauno: The Italian Large Language Model that will leave you senza parole!

arXiv

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arXiv 2023年

作者： Bacciu, Andrea Trappolini, Giovanni Santilli, Andrea Rodolà, Emanuele Silvestri, Fabrizio Sapienza University Department of Computer Control and Management Engineering Italy Sapienza University Computer Science Department Italy

This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian, demonstrating that obtaining a fine-tuned conversational bot with a single GPU is possible. In addition, we release a collection of datasets for conversational AI in Italian. The datasets on which we fine-tuned Fauno include various topics such as general question answering, computer science, and medical questions. We release our code and datasets on https://***/RSTLess-research/Fauno-Italian-LLM © 2023, CC BY.

关键词： Computational linguistics

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Delayed Transitions in GPenSIM 1st

Delayed Transitions in GPenSIM

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1st International Symposium on Systems Modelling and Simulation, SMS 2024

作者： Feng, Yuming Samolej, Slawomir Davidrajuh, Reggie School of Computer Science and Engineering Chongqing Three Gorges University Wenzhou China Department Computer and Control Engineering Rzeszow University of Technology Rzeszow Poland Department Electrical Engineering and Computer Science University of Stavanger Stavanger Norway

ISBN: (纸本)9789819646128

The General-purpose Petri Net Simulator (GPenSIM) is a tool for modeling, simulation, and performance analysis of discrete event systems. GPenSIM is specially designed to model real-life industrial systems. Hence, the transitions in GPenSIM represent real-life events, such as machines. However, this induces a problem, namely that an enabled transition consumes tokens from its input places immediately. This act of an enabled transition immediately consuming tokens gives only monetarily visibility to the tokens, which can be a problem for certain cases where we need to see the tokens in their input places for a while. This paper proposes a method to delay an enabled transition ("delayed transitions") so that the tokens it is supposed to consume from its input places will be visible for a while. This paper, after presenting the theory and the method, also presents an application (the cat and mouse problem) of the delayed transitions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete event simulation

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Enhancing Efficiency and Safety Through Autonomous Navigation in the Intensive Care Unit 8

Enhancing Efficiency and Safety Through Autonomous Navigatio...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Ponzi, Valerio Puglisi, Adriano Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The use of autonomous systems in Intensive Care Units (ICUs) has become incredibly important, especially during the COVID-19 pandemic. This period has overwhelmed both ICUs and hospitals, halting many other medical activities and causing significant challenges. This project aims to develop a navigation system tailored specifically for the ICU environment, adapting it to the unique procedures and regulations of that setting. Due to the critical conditions of ICU patients, strict rules dictate precise requirements for navigation, necessitating a context-specific approach. This work will propose a comprehensive navigation system capable of safely guiding from point A to point B within an ICU while addressing the critical issues present in such environments. Unlike traditional Nav2 systems, it will feature specialized collision avoidance components designed specifically for ICU settings, taking into account both contextual demands and the chosen approach. This will involve implementing a multilayered protection technique and employing active movements to prevent collisions with dynamic obstacles. © 2023 Copyright for this paper by its authors.

关键词： Reinforcement learning

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