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An enhanced LSHADE-based algorithm for global and constrained optimization in applied mechanics and power flow problems

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Swarm and Evolutionary Computation 2025年 97卷

作者： Karam M. Sallam Ibrahim Alrashdi Reda Mohamed Mohamed Abdel-Basset Department of Computer Science University of Sharjah Sharjah United Arab Emirates Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka 2014 Saudi Arabia Faculty of Computers and Informatics Zagazig University Zagazig Sharqiyah 44519 Egypt

This study proposes a new evolutionary algorithm, namely NL-SHADE, that combines the linear population size reduction-based SHADE (L-SHADE) with the Nutcracker Optimization Algorithm (NOA) to better solve global optimization and real-world constrained optimization problems. Several optimization algorithms have been developed in the literature to address these issues. However, they still stall in local optima and exhibit slow convergence speed, which are the main limitations that motivate us to propose the NL-SHADE algorithm. In this algorithm, the SHADE algorithm is responsible for the exploration operator in the early stages of the optimization process to avoid stagnation in local optima, while NOA is responsible for improving convergence speed. Furthermore, at the end of each generation, the linear population size reduction method is used to exclude some inferior solutions that might lead to local optima and reduce convergence speed. To solve the constrained optimization problems, NL-SHADE is combined with a gradient-based repair method to propose a new variant, rNL-SHADE, which uses gradient information from the constraint set to direct infeasible solutions into feasible regions. In this study, two experiments are conducted. In the first experiment, the proposed NL-SHADE is evaluated using two unconstrained CEC benchmarks, CEC2017 and CEC2020, and compared with numerous cutting-edge algorithms using several performance metrics. In the second experiment, the performance of the proposed algorithms is also tested by solving 29 RWC optimization problems from four different domains. The experimental findings demonstrate that, for the majority of the solved RWC problems, rNL-SHADE can perform better than all compared algorithms.

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Why Fuzzy Control Is Often More Robust (and Smoother): A Theoretical Explanation

Why Fuzzy Control Is Often More Robust (and Smoother): A The...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Orsolya Csiszár Gábor Csiszár Olga Kosheleva Martine Ceberio Vladik Kreinovich Research Center for Complex Systems Aalen University Institute of Applied Mathematics John von Neumann Faculty of Informatics Óbuda University Biomatics and Applied AI Institute John von Neumann Faculty of Informatics Óbuda University Department of Teacher Education University of Texas at El Paso El Paso Texas USA Department of Computer Science University of Texas at El Paso El Paso Texas USA

In many practical situations, practitioners use easier-to-compute fuzzy control to approximate the more-difficult-to-compute optimal control. As expected, for many characteristics, this approximate control is slightly worse than the optimal control it approximates. However, with respect to robustness or smoothness, the approximating fuzzy control is often better than the original one. In this paper, we provide a theoretical explanation for this somewhat mysterious empirical phenomenon.

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Comparative Analysis of AES, RC and Twofish Algorithms for Data Encryption and Decryption

Comparative Analysis of AES, RC and Twofish Algorithms for D...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Ahmad Faizal Bin Tajuddin Mohammad Hafiz Mohd Yusof Mohammed Balfaqih Akram A. Almohammedi Sam Darshi Nadhem Qaid College of Computing Informatics and Mathematics Universiti Teknologi MARA UiTM Tapah Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Electrical and Electronic Engineering Department Karabük University Karabük Turkey Department of Electrical Engineering Indian Institute of Tehcnology Ropar Punjab India R&D Engineer Lead Marine Powertrain Controls & Electronics Caterpillar Inc Houstan Texas USA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

This paper presents a comparative analysis of three widely used encryption algorithms, namely AES (Advanced Encryption Standard), RC (Rivest Cipher), and Twofish, with a focus on their performance in data encryption and decryption. The study aims to evaluate the strengths and weaknesses of these algorithms in terms of security, speed, and efficiency. The analysis involves benchmarking the algorithms against various performance metrics, including encryption time, decryption time, key size, and resistance against cryptanalysis attacks. Additionally, the research explores the impact of different data sizes and configurations on the performance of each algorithm. The findings of this study will provide valuable insights for decision-makers and practitioners in selecting the most suitable encryption algorithm for their specific needs, considering the tradeoffs between security and performance.

关键词： Resistance Ciphers Three-dimensional displays Data security Benchmark testing Encryption Timing Information technology Standards

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Optimizing Customer Response Prediction in Auto Insurance: A Comparative Study of Machine Learning Models

Optimizing Customer Response Prediction in Auto Insurance: A...

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Cyber Resilience (ICCR), International Conference on

作者： Sanad Aburass Osama Dorgham Jamil Al Shaqsi Maha Abu Rumman Ahmed Al Zarouni Department of Computer Science Maharishi International University Fairfield Iowa USA School of Information Technology Skyline University College Sharjah United Arab Emirates Prince Abdullah bin Ghazi Faculty of ICT Al-Balqa Applied University Al-Salt Jordan Information Systems Department Sultan Qaboos University Muscat Oman

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This study examines various machine learning models to predict customer responses in the auto insurance industry. We focus on metrics like accuracy, precision, recall, and F1-score, carefully selecting threshold values to balance model performance with practical business applications. Our analysis reveals the XGB Classifier's superiority, achieving 99% accuracy and a 98% F1-score. We provide a comparative analysis of models, highlighting the XGB Classifier's strengths in handling complex data and its efficiency compared to other tested models, like Gaussian NB and Logistic Regression, which showed similar accuracies but varied in precision and recall. This study underscores the importance of choosing the right model and fine-tuning it for specific industry needs.

关键词： Industries Measurement Analytical models Insurance Predictive models Data models Automobiles

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Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism

arXiv

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arXiv 2022年

作者： Hong, Haokai Jiang, Min Feng, Liang Lin, Qiuzhen Tan, Kay Chen School of Informatics Xiamen University Fujian China College of Computer Science Chongqing University Chongqing China College of Computer Science and Software Engineering Shenzhen University Guangdong China Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Artificial Intelligence Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Fujian361005 China

Large-scale multiobjective optimization problems (LSMOPs) refer to optimization problems with multiple conflicting optimization objectives and hundreds or even thousands of decision variables. A key point in solving LSMOPs is how to balance exploration and exploitation so that the algorithm can search in a huge decision space efficiently. Large-scale multiobjective evolutionary algorithms consider the balance between exploration and exploitation from the individual's perspective. However, these algorithms ignore the significance of tackling this issue from the perspective of decision variables, which makes the algorithm lack the ability to search from different dimensions and limits the performance of the algorithm. In this paper, we propose a large-scale multiobjective optimization algorithm based on the attention mechanism, called (LMOAM). The attention mechanism will assign a unique weight to each decision variable, and LMOAM will use this weight to strike a balance between exploration and exploitation from the decision variable level. Nine different sets of LSMOP benchmarks are conducted to verify the algorithm proposed in this paper, and the experimental results validate the effectiveness of our design. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Learning to discuss strategically: a case study on one night ultimate werewolf 24

Learning to discuss strategically: a case study on one night...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Xuanfa Jin Ziyan Wang Yali Du Meng Fang Haifeng Zhang Jun Wang Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Cooperative AI Lab Department of Informatics King's College London University of Liverpool Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Nanjing Artificial Intelligence Research of IA AI Centre Department of Computer Science UCL

ISBN: (纸本)9798331314385

Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs), recent agents built with these often neglect the control over discussion tactics, which are essential in communication scenarios and games. As a variant of the famous communication game Werewolf, One Night Ultimate Werewolf (ONUW) requires players to develop strategic discussion policies due to the potential role changes that increase the uncertainty and complexity of the game. In this work, we first present the existence of the Perfect Bayesian Equilibria (PBEs) in two scenarios of the ONUW game: one with discussion and one without. The results showcase that the discussion greatly changes players' utilities by affecting their beliefs, emphasizing the significance of discussion tactics. Based on the insights obtained from the analyses, we propose an RL-instructed language agent framework, where a discussion policy trained by reinforcement learning (RL) is employed to determine appropriate discussion tactics to adopt. Our experimental results on several ONUW game settings demonstrate the effectiveness and generalizability of our proposed framework. The project page of our paper: ***.

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Deep Learning-Based Leaf Image Analysis for Tomato Plant Disease Detection and Classification

Deep Learning-Based Leaf Image Analysis for Tomato Plant Dis...

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IEEE International Conference on Image Processing

作者： Ammar Chouchane Abdelmalik Ouamanea El Ouanas Belabbaci Yassine Himeur Abbes Amira University Center of Barika Amdoukal Road Barika Algeria Laboratory of LI3C University of Biskra Biskra Algeria Laboratory of Medical Informatics (LIMED) Faculty of Technology University of Bejaia Bejaia Algeria College of Engineering and Information Technology University of Dubai Dubai UAE Department of Computer Science University of Sharjah UAE

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Tomato plant disease detection and classification, utilizing leaf images through deep learning, intersects the fields of plant pathology and agriculture. Deep learning has demonstrated significant potential in accurately identifying and classifying various plant diseases from leaf images. In this study, we introduce a hybrid system that combines a potent machine learning algorithm, Exponential Discriminant Analysis (EDA), with a transfer learning process leveraging recent and advanced deep networks, including ResNet50, Darknet53, DenseNet201, and EfficientNetB0. This system was evaluated using two challenging datasets: Taiwan and PlantVillage tomato leaf datasets. The experimental results underscore the high competitiveness of the proposed method, achieving mean accuracies of 98.29% and $\mathbf{9 8. 0 9 \%}$ on these datasets, respectively.

关键词： Deep learning Plant diseases Pathology Machine learning algorithms Image analysis Accuracy Plants (biology)

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Multiregion Multiscale Image Segmentation with Anisotropic Diffusion 25th

Multiregion Multiscale Image Segmentation with Anisotropic D...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Prasath, V. B. Surya Thanh, Dang Ngoc Hoang Hai, Nguyen Hoang Dvoenko, Sergey Department of Electrical Engineering and Computer Science University of Cincinnati CincinnatiOH45221 United States Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Biomedical Informatics College of Medicine University of Cincinnati CincinnatiOH45267 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH45257 United States Department of Information Technology School of Business Information Technology University of Economics Ho Chi Minh City Ho Chi Minh City Viet Nam Department of Computer Science Vietnam-Korea University of Information and Communication Technology – The University of Danang Danang Viet Nam Department of Information Security Institute of Applied Mathematics and Computer Science Tula State University Tula city Russia

ISBN: (纸本)9783030688202

We present a multiregion image segmentation approach which utilizes multiscale anisotropic diffusion based scale spaces. By combining powerful edge preserving anisotropic diffusion smoothing with isolevel set linking and merging, we obtain coherent segments which are tracked across multiple scales. A hierarchical tree representation of the given input image with progressively simplified regions is used with intra-scale splitting and inter-scale merging for obtaining multiregion segmentations. Experimental results on natural and medical images indicate that multiregion, multiscale image segmentation (MMIS) approach obtains coherent segmentation results. © 2021, Springer Nature Switzerland AG.

关键词： Diffusion

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Complexity-Constrained Quantum Thermodynamics

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PRX Quantum 2025年第1期6卷 010346-010346页

作者： Anthony Munson Naga Bhavya Teja Kothakonda Jonas Haferkamp Nicole Yunger Halpern Jens Eisert Philippe Faist Joint Center for Quantum Information and Computer Science NIST and University of Maryland College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA Grup d’Informació Quàntica Departament de Física Universitat Autònoma de Barcelona 08193 Bellaterra (Barcelona) Spain Dahlem Center for Complex Quantum Systems Freie Universität Berlin 14195 Berlin Germany School of Engineering and Applied Sciences Harvard University Massachusetts 02134 USA Institute for Physical Science and Technology University of Maryland College Park Maryland 20742 USA

Quantum complexity measures the difficulty of realizing a quantum process, such as preparing a state or implementing a unitary. We present an approach to quantifying the thermodynamic resources required to implement a process if the process’s complexity is restricted. We focus on the prototypical task of information erasure, or Landauer erasure, wherein an n-qubit memory is reset to the all-zero state. We show that the minimum thermodynamic work required to reset an arbitrary state in our model, via a complexity-constrained process, is quantified by the state’s complexity entropy. The complexity entropy therefore quantifies a trade-off between the work cost and complexity cost of resetting a state. If the qubits have a nontrivial (but product) Hamiltonian, the optimal work cost is determined by the complexity relative entropy. The complexity entropy quantifies the amount of randomness a system appears to have to a computationally limited observer. Similarly, the complexity relative entropy quantifies such an observer’s ability to distinguish two states. We prove elementary properties of the complexity (relative) entropy. In a random circuit—a simple model for quantum chaotic dynamics—the complexity entropy transitions from zero to its maximal value around the time corresponding to the observer’s computational-power limit. Also, we identify information-theoretic applications of the complexity entropy. The complexity entropy quantifies the resources required for data compression if the compression algorithm must use a restricted number of gates. We further introduce a complexity conditional entropy, which arises naturally in a complexity-constrained variant of information-theoretic decoupling. Assuming that this entropy obeys a conjectured chain rule, we show that the entropy bounds the number of qubits that one can decouple from a reference system, as judged by a computationally bounded referee. Overall, our framework extends the resource-theoretic approach to thermo

关键词： Qubits

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Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments

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Computational Intelligence and Neuroscience 2022年第0期2022卷 5012962页

作者： Hameed Abdulkareem, Karrar Awad Mutlag, Ammar Musa Dinar, Ahmed Frnda, Jaroslav Abed Mohammed, Mazin Hasan Zayr, Fawzi Lakhan, Abdullah Kadry, Seifedine Ali Khattak, Hasan Nedoma, Jan College of Agriculture Al-Muthanna University Samawah66001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Ministry of Education General Directorate of Curricula Pure Science Department Baghdad Iraq Engineering Department University of Technology- Iraq Baghdad Iraq Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and Communication University of Žilina Žilina Slovakia Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Poruba Ostrava Czech Republic College of Computer Science and Information Technology University of Anbar 11 Ramadi Anbar Iraq Department of Biochemistry College of Medicine University of Wasit Wasit Iraq Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China Noroff University College Kristiansand Norway Islamabad44500 Pakistan

COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in

关键词： COVID-19

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