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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Guo, Shengzhuo Du, Qingguo Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Metasurface can be used to manipulate the polarization, amplitude, phase of electromagnetic waves which has been applied in various fields such as holography technology, imaging and sensing. However, traditional metasurface optimization methods such as parameters scanning require a considerable amount of time and computing power and cannot cover every set of parameters due to the setting of step size, which may lead to the optimization result falling into a local optimal solution. To address the above-mentioned issues, in this paper, we propose a method that combines transfer learning optimization network with grey wolf optimization algorithm for structural parameters optimization. Using deep learning networks can significantly improve the speed of spectral prediction, while transfer learning algorithms can enhance the prediction accuracy of the networks. The grey wolf optimization algorithm belongs to a global optimization algorithm and its performance is superior to other traditional algorithms, thus enabling it to achieve a wider bandwidth. The results show that the bandwidth of the transmission spectrum obtained through the grey wolf optimization algorithm is 87.37 nm, which is wider than that achieved through traditional method of parameter scanning. At the same time, it only takes 45 minutes, which is one-seventieth of the time required by traditional optimization methods.

关键词： Metasurface Transfer Learning optimization algorithm

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A new multi-resolution optimization algorithm for PET - CT cardiac images registration

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11th International Congress of the IUPESM/World Congress on Medical Physics and Biomedical Engineering

作者： Marinelli, M. Tucci, F. Positano, V. Neglia, D. Landini, L. Scuola Super Sant Anna Piazza Martiri Liberta 33 Pisa Italy Univ Pisa Dept Informat Engn I-56100 Pisa Italy G Monasterio Fdn Pisa Italy

ISBN: (纸本)9783642038815

Cardiac image fusion is the combination of medical images of heart acquired by different modalities. A prerequisite for image fusion is the image registration process, that allows the spatial alignment of the volumetric data sets involved in the fusion. In this paper we present a registration algorithm able to align positron emission tomography (PET) and computed tomography (CT) cardiac images. The registration method was developed by hypotizing a rigid transformation between data sets and using the mutual information (MI) as registration metric. The core of the study is the development of a new and robust optimization algorithm that allowed to determine the global maximum of MI and to define the correct spatial transformation. The optimization algorithm exploits a multi-resolution method. In the first step, a global optimization method (i.e. genetic algorithm) explores the entire space of possible transformations to find a solution near the global maximum of the MI. In the second step, a local optimizer (i.e. simplex method) refines the search finding the global maximum. Different data resolutions and interpolation algorithms were adopted in the two steps to achieve reasonable processing times. The developed algorithm was tested on two volumetric PET and CT cardiac software models and on PET and CT real data sets. The developed optimization algorithm allows to reach the global maximum of the similarity function and to determine the correct spatial alignment in both the synthetic and real data sets.

关键词： Image fusion registration process optimization algorithm genetic algorithm

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Discussion on Knapsack Problem optimization algorithm Based on Complex Network

Discussion on Knapsack Problem Optimization Algorithm Based ...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Xiao Meng Zhou Yunyao Wuhan Univ Technol Int Educ Coll Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Informat Engn Coll Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9783038351153

This passage is to put forward knapsack problem optimization algorithm based on complex network(KOABCN). Knapsack problem has extremely wide application in a great number of fields. For instance, knapsack problem can be applied in information coding, budget control, project choosing, material cutting, cargo loading and unloading as well as Internet information safety. Since 1950's, knapsack problem has been one of the most heated topics in algorithm and complexity research. Therefore, knapsack will still be largely focused in the next period of research.

关键词： Knapsack problem optimization algorithm complex network

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EVOLVING SUPPORT VECTOR MACHINE USING MODIFIED FRUIT FLY optimization algorithm AND GENETIC algorithm FOR BINARY CLASSIFICATION PROBLEM 13

EVOLVING SUPPORT VECTOR MACHINE USING MODIFIED FRUIT FLY OPT...

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13th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Ye, Fei Lou, Xin-Yuan Han, Min Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China Chengdu Univ Informat Technol Sch Software Engn Chengdu 610000 Sichuan Peoples R China

ISBN: (纸本)9781509061266

Binary classification problem is one of the mainstream research in pattern recognition field. This study proposed a modified fruit-fly optimization algorithm (FOA), which can find an eligible begin location of the FOA as starting location before running the FOA's procedure, and in the FOA's processing, the SVM parameters is modified by dynamically updating the location of each fruit-fly and the optimal feature subset is changed by evolutionary process of genetic algorithm (GA) at the same time. In the proposed method, a weighted objective function is designed to evaluate population and to take account the trade-off between sensitivity and specificity. The best individual is used to guide for evolutionary process of FOA and GA. To evaluate the classification performance of the proposed approach, this study designs several groups of comparative experiments using the proposed approach with the well-known methods, and the four binary classification datasets from the UCI Machine Learning data repository are used for the experiments. The empirical results show that the proposed approach achieves better results in terms of classification performance and a low computational cost on solving the binary classification problems.

关键词： Tracking support vector machines fruit fly optimization genetic algorithm optimization algorithm binary classification

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Short-term wind speed prediction based on deep learning and intelligent optimization algorithm 6

Short-term wind speed prediction based on deep learning and ...

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Guan, Peilong Wu, Zixi Guangdong Univ Technol Sch Management Guangzhou Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

In order to improve the accuracy of wind speed prediction, a wind speed prediction model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), long short-term memory (LSTM) and gray wolf optimization (GWO) algorithm was proposed from the perspective of reducing wind speed nonstationarity and optimizing combination weight. First, CEEMDAN was used to decompose the observed wind speed into a series of sub-sequences reflecting the characteristics of the original wind speed. Then the subsequence is predicted by LSTM, and the predicted value of the subsequence is output. Finally, the combined weight of the sub-sequences was optimized by GWO, and the sub-sequences were combined to obtain the wind speed prediction results. The experimental results show that CEEMDAN-LSTM-GWO wind speed prediction model proposed in this study has better performance than the comparison model.

关键词： wind speed prediction data decomposition deep learning optimization algorithm

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An enhanced donkey and smuggler optimization algorithm for choosing the precise job applicant

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Iran Journal of Computer Science 2023年第3期6卷 233-243页

作者： Hasan, Nazir M. Rashid, Tarik A. Alsadoon, Abeer Qosaeri, Ahmed S. Abualigah, Laith Yaseen, Zaher Mundher Computer Science and Engineering Department University of Kurdistan Hawler KR Erbil Iraq Charles Sturt University Sydney Australia Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

Throughout the last few decades, Nature-Inspired algorithms (NIA) have become very popular in solving real-world problems by getting inspiration from nature. This work suggests the Modified Donkey and Smuggler optimization (MDSO) algorithm for solving the selection problem to choose suitable job applicants for a specific position. The original Donkey and Smuggler optimization algorithm (DSO) has two modes: smuggler and donkey mode (non-adaptive and adaptive). In the smuggler mode, the algorithm tries to find the best solutions, which is the path to send the donkey to the destination;once the best path is found, the smuggler will send the donkey through the selected path. The donkey's actions will start when the smuggler mode's best solution is no longer the best one. Certain modifications have been made in the smuggler mode, including replacing the original fitness with a new fitness equation since that method can work more accurately. The Human Resource (HR) department in Korek-telecom has been used as a resource to achieve real-world data to test the original and Modified DSO. Using MDSO, not only will organizations be able to choose suitable job applicants more accurately but also will manage to do so at a faster pace as well. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Donkey and smuggler algorithms Human resource management MDSO Nature-inspired algorithms optimization algorithm Swarm intelligence

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Fault diagnosis optimization algorithm based on k nearest neighbor 31

Fault diagnosis optimization algorithm based on k nearest ne...

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31st Chinese Control And Decision Conference (CCDC)

作者： Yang Yinghua Wang Xiulong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781728101057

In the actual industrial process, it is the key to recognize the fault variables accurately as soon as possible after the fault is detected. Recently, a fault variable recognition method based on k-nearest neighbor reconstruction (FVR-kNN) has been proposed. However, dealing with fault problem caused by multiple variables, the algorithm needs to exhaustive the arrangement of all variables, resulting in high complex computation. And the multivariate estimation in FVR-KNN is not accurate. Thus, this paper proposes a variable recognition optimization algorithm based on FVR-kNN (OFVR-kNN). It optimizes the estimation steps of FVR-kNN in reconstructing multivariate, guaranteeing that the estimations of these potential fault variables have no mutual influence. According to the fault magnitude in corresponding direction, the fault variables are selected in turn. OFVR-kNN does not need to exhaustive all the combinations, greatly reducing the number of reconstructions in fault sample. In this paper, the validity of the optimization method is proved in Tennessee Eastman process.

关键词： Fault recognition KNN optimization algorithm Tennessee Eastman process (TEP)

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Comparative study of microgrid optimal scheduling under multi-optimization algorithm fusion 10

Comparative study of microgrid optimal scheduling under mult...

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10th International Forum on Electrical Engineering and Automation, IFEEA 2023

作者： Duan, Hongyi Li, Qingyang Li, Yuchen Ji, Tianjiao Zhang, Jianan Xie, Yuming Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xi'an China Shanghai University of Finance and Economics University School of Mathematica Shanghai China National University of Defense Technology Faculty of Electronic Science Changsha China

ISBN: (纸本)9798350309379

As global attention on renewable and clean energy grows, the research and implementation of microgrids become paramount. This paper delves into the methodology of exploring the relationship between the operational and environmental costs of microgrids through multi-objective optimization models. By integrating various optimization algorithms like Genetic algorithm, Simulated Annealing, Ant Colony optimization, and Particle Swarm optimization, we propose an integrated approach for microgrid optimization. Simulation results depict that these algorithms provide different dispatch results under economic and environmental dispatch, revealing distinct roles of diesel generators and micro gas turbines in microgrids. Overall, this study offers in-depth insights and practical guidance for microgrid design and operation. © 2023 IEEE.

关键词： Microgrid Multi-objective Dispatchs multi-target optimization algorithm

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SSALEO optimization algorithm for Feature Selection for Diabetic Retinopathy 24

SSALEO Optimization Algorithm for Feature Selection for Diab...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Salem, Mohammed A. Alijla, Basem O. Islamic University of Gaza Palestine

ISBN: (纸本)9798350384307

Diabetic Retinopathy is a severe health problem in the modern world that affects people of all ages and can result in blindness. Many of the affected persons won't lose their eyesight if Diabetic Retinopathy can be identified in its early stages. Therefore, several studies employ several classification algorithms in order to detect DR disease in its early stages. In this study, we use the SSALEO optimization algorithm as a Multi-Objective Feature Selection algorithm in order to obtain higher accuracy with the minimum number of most informative features (i.e., feature reduct). The APTOS-2019 Gaussian-Filtered dataset was used which contains 3662 images. In the proposed model, features were extracted from the input images using a novel feature extraction technique called 'FE-CM'. Feature Selection was performed using SSALEO optimization algorithms as a wrapper-based feature selection using the KNN classifier. the performance of SSALEO was compared using five well-known optimization algorithms: PSO, GWO, MFO, WOA, and BAT. The findings confirm the capability of the SSALEO algorithm as a Feature Selection algorithm in order to help ophthalmologists to detect the disease accurately. © 2023 IEEE.

关键词： Diabetic Retinopathy Feature Selection optimization algorithm SSALEO

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Protective Project Decision Model Based on Critic-Topsis and optimization algorithm 11th

Protective Project Decision Model Based on Critic-Topsis and...

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11th International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Liang, Zhengrong Wu, Congcong Li, Jinkang College of Physics and Communication Electronics Jiangxi Normal University Nanchang China

This paper deals with one of the dire needs of wildlife conservation: effective decision-making frameworks. Growing global connectivity and economic development result in growing demand for certain wildlife products, causing enormous ecological damage and losses in biodiversity. This three-dimensional model includes evaluation based on power, resources, and interest for finding the best partners for conservation projects using the Critic-Topsis model combined with optimization algorithms. The model is then applied using a case study of the United States' terrestrial and inland water reserves. Data used in the study were sourced from 21 countries and organizations identifying key stakeholders like governments, specifically that of the US. The research showed that successful conservation would only be reached by cooperation between governments, NGOs, and other stakeholders that need to collaborate on the same platform. The findings show that strategic and data-driven approaches can make a huge difference in enhancing the impact of wildlife protection initiatives. Conclude that further research into and adaptation of these models must be continued to respond to the ever-changing challenges that surround wildlife conversation, sustainable development, and ecological balance. This forms a very strong foundation for future planning and implementation of the conservation plan. © 2024 The Authors. Published by Elsevier B.V.

关键词： Conservation Plan Critic-Topsis Data-Driven optimization algorithm Protective Project

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