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Archive Based Multi-Swarm Algorithm for Many-Objective Problems 3

Archive Based Multi-Swarm Algorithm for Many-Objective Probl...

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3rd Brazilian conference on intelligent Systems (BRACIS)

作者： Britto, Andre Mostaghim, Sanaz Pozo, Aurora Univ Fed Sergipe Dept Computat Sao Cristovao Sergipe Brazil Univ Magdeburg Fac Comp Sci Inst IWS D-39106 Magdeburg Germany Univ Fed Parana Dept Comp Sci BR-80060000 Curitiba Parana Brazil

ISBN: (纸本)9781479978595

Multi-Objective optimization Problems (MOPS) present several challenges. In particular, when the number of objectives is greater than three, they are actually called Many-Objective optimization Problems (MaOPs). To overcome this limitation, researches are investigating multi-swarm approaches. Multi-swarm is a very interesting approach that allows the decomposition of different aspects of the problem and each swarm could specialize on a dedicate part of the problem. This study explores this idea to create a novel multi-swarm algorithm, called A-Multi, which tackles the main challenge of MaOPs: to convergence towards the true Pareto front and to diversify the obtained solutions covering the entire Pareto front. A-Multi project is based on different swarms use different archiving methods, ones specialized on diversity and others specialized on convergence. The algorithm is evaluated with several MaOPs in terms of both convergence and diversity and the results shows the validity of archive based multi-swarm approach.

关键词： Algorithm design and analysis Convergence Electronic mail Linear programming optimization Sociology Statistics Many-Objective optimization Multi-Swarm Algorithm Particle Swarm Optmization Algorithm design and analysis electronic mail Converge Sociology linear programming archives

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Using OR-Tools When Solving the Nurse Scheduling Problem 3rd

Using OR-Tools When Solving the Nurse Scheduling Problem

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3rd International conference on optimization, learning Algorithms and Applications (OL2A)

作者： Oliveira, Marcia Rocha, Ana Maria A. C. Alves, Filipe Univ Minho ALGORITMI Res Ctr LASI P-4710057 Braga Portugal Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Regioes M Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9783031530241;9783031530258

Scheduling of employees is a common problem that can be found in most organizations all over the world. One example is the nurse scheduling problem (NSP), which is a complex combinatorial opti-mization problem faced by healthcare institutions in assigning working and nonworking days. The NSP comprises constraints for the nurses, for the hospital and considers specific labor regulations, as well as the skills and preferences of workers. In summary, it involves hard and soft constraints. It is essential to create a quality timetable that can lead to a more contented and thus, more effec-tive and productive workforce. To improve this process, it can be used automated approaches and techniques. In this study, a litera-ture review about the nurse scheduling problem and how to use the Google OR-Tools software to solve it is performed. Moreover, an example of an NSP involving 10 nurses being assigned to three shifts a day, seven days a week is presented. Some condi-tions/constraints have been added in order to reproduce a real situation.

关键词： Nurse scheduling problem Constrained optimization Google OR-Tools

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An efficient coevolutionary algorithm using dynamic species control

An efficient coevolutionary algorithm using dynamic species ...

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3rd International conference on Natural Computation (ICNC 2007)

作者： Kim, Myung Won Ryu, Joung Woo Soongsil Univ Sch Comp 511 Sangdo Dong Seoul South Korea Intelligent Robot Res Division ETRI 161 Daejeon 305700 South Korea

ISBN: (纸本)0769528759

A coevolutionary algorithm is an extention of the conventional genetic algorithm that incorporates the strategy of divide and conquer in developing a complex solution in the form of interacting co-adapted subcomponents. In this paper we propose an efficient coevolutionary algorithm dynamically controlling species splitting and merging. Our algorithm conducts efficient local search in the reduced search space by splitting species for independent variables while it conducts global search by merging species for interdependent variables. We have experimented the proposed algorithm with some benchmarking function optimization problems and the inventory control problem, and have shown that the algorithm outperforms the existing coevolutionary algorithms.

关键词： Dynamical systems

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CARTOONNET: Caricature Recognition of Public Figures 3rd

CARTOONNET: Caricature Recognition of Public Figures

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3rd International conference on Computer Vision and Image Processing (CVIP)

作者： Shukla, Pushkar Gupta, Tanu Singh, Priyanka Raman, Balasubramanian Univ Calif Santa Barbara Santa Barbara CA 93106 USA Indian Inst Technol Roorkee Roorkee Uttar Pradesh India

ISBN: (纸本)9789813290884;9789813290877

Recognizing faces in the cartoon domain is a challenging problem since the facial features of cartoon caricatures of the same class vary a lot from each other. The aim of this project is to develop a system for recognizing cartoon caricatures of public figures. The proposed approach is based on the Deep Convolutional Neural Networks (DCNN) for extracting representations. The model is trained on both real and cartoon domain representations of a given public figure, in order to compensate the variations in the same class. The IIIT-CFW (Mishra et al., European conference on computer vision, 2016) [1] dataset, which includes caricatures of public figures, is used for the experiments. It is seen from these experiments that improving the performance of the model can be achieved when it is trained on representations from both real and cartoon images of the given public figure. For a total of 86 different classes, an overall accuracy of 79.65% is achieved with this model.

关键词： Face recognition Cartoon recognition Deep learning Convolutional Neural Networks

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Research on intelligent Wearable Detection Method Based on Machine Vision 3

Research on Intelligent Wearable Detection Method Based on M...

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3rd IEEE International conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

作者： Shao, Xintong Li, Haixia Liu, Shengxian Department of Aviation Maintenance Shanghai Civil Aviation College Shanghai China Training Division Shanghai Xinli Machinery Factory Co.. Ltd. Shanghai China IOTG Intel APAC Research and Development Ltd. Shanghai China

ISBN: (纸本)9781665425186

The working site of aircraft maintenance personnel is an important part of the entire airport operation. Therefore, it is necessary to supervise whether maintenance personnel comply with safety regulations during the course of their work. The most important point of all is to check whether maintenance personnel's clothing complies with related specifications. According to the requirements of current epidemic prevention, we have made higher standards on the dressing of maintenance personnel, especially for those who enter the key areas of the cabin. As we all know, traditional manual safety supervision requires monitoring on the maintenance site. However, due to the large number of maintenance sites, supervisors are not able to check all the workers' operations simultaneously. Thus violations cannot be obtained timely, which is bound to cause ineffective supervision. According to the characteristics of the application scenario, this paper proposes an intelligent maintenance crew clothing detection method based on the OpenVINO platform, which can analyze and identify the clothing requirements of maintenance personnel in real time, and promptly warn the clothing that does not meet the safety regulations;in the training of the samples, Using the YOLO algorithm to perform machine learning on the existing model, which can quickly and accurately find problems related to unsafe dressing at the aircraft maintenance site. © 2021 IEEE

关键词： Maintenance

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Learners grouping improvement in e-learning environment using fuzzy inspired PSO method

Learners grouping improvement in e-learning environment usin...

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3rd International conference on E-learning and E-Teaching (ICELET)

作者： Ghorbani, Fatemeh Montazer, Gholam Ali Tarbiat Modares Univ Sch Engn Dept Informat Technol Tehran Iran

ISBN: (纸本)9781467309585;9781467309561

Recent advances in technology and the integration of these advances in instructional design have led to a mass individualization where personalized instruction is offered simultaneously to large groups of learners. The first step to adapt instruction to group of learners is learners grouping. Many methods have used to group learners in e-learning environment specially data mining techniques such as clustering methods. This paper aims to propose a clustering method to group learners using some specific learners' observable behavior while working by system and based on cognitive style. The objective function of proposed method is defined by considering two criteria in measuring the clustering goodness, compactness and separation, and Particle Swarm optimization (PSO) method is used to optimize the objective function. This method used to group learners based on cognitive style. Results of the proposed method are compared with K-means, fuzzy C-means, and EFC methods using Davies-Bouldin cluster validity index and comparing the achieved groups and the cognitive style of learners who are in the same group, shows that the grouping accuracy is in a higher level using fuzzy-inspired PSO method and this method has the better clustering performance than the others and groups similar learners in one cluster.

关键词： e-learning System cognitive Style Grouping Fuzzy clustering Particle Swarm optimization (PSO)

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Comparative Study of Conjugate Gradient to Optimize learning Process of Neural Network for Intrusion Detection System (IDS) 3

Comparative Study of Conjugate Gradient to Optimize Learning...

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3rd International conference on Science in Information Technology (ICSITech)

作者： Wisesty, Untari N. Adiwijaya Telkom Univ Sch Comp Bandung Indonesia

ISBN: (纸本)9781509058662

Intrusion Detection System (IDS) is a device or software application that monitors and inspects all inbound and outbound network activities, identifies suspicious patterns that may be a network attack from someone attempting to break into or compromise a system. IDS categorize in two ways, namely misuse detection and anomaly detection. In this research, intrusion detection was done by anomaly detection. The data used is KDDCUP dataset 1999, which has 4 types of attacks, namely DoS, U2R, R2L, and Prob. Anomaly detection was built using Backpropagation algorithm optimized by Conjugate Gradient algorithm. In this paper, it was implemented and analyzed the use of CG optimization (Fletcher Reeves, Polak Ribiere, Powell Beale) in the process of backpropagation learning for IDS. Moreover, to minimize the performance of learning rate parameter, four types of line search techniques used, i.e. Brent Search, Golden Section Search, Charalambous Search and Hybrid Bisection-cubic Search. Based on the experiment results, the proposed scheme gives the best accuracy when the data is divided into two classes with a proportion of normal and intrusion data was balanced, with an average accuracy of 93.2%. Meanwhile, the multi class classification model has an average f-measure of 54.13%. The best performance was obtained by using Conjugate Gradient-Fletcher Reeves optimization method and line search method Hybrid Bisection-Cubic Search.

关键词： intrusion detection system conjugate gradient line search cackpropagation neural network

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Development of Repetitively Enhanced Neural Networks (RENN) for efficient missile design and optimization

Development of Repetitively Enhanced Neural Networks (RENN) ...

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3rd International Joint conference on Computational Sciences and optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Nguyen, Nhu-Van Jeon, Kwon-Su Lee, Jae-Woo Byun, Yung-Hwan Aerospace Information Engineering Konkuk University Seoul Korea Republic of

ISBN: (纸本)9780769540306

An improved approach for design optimization of air intercept missile is developed and presented. A Bayesian learning technique is mapped into Back-propagation neural networks (BPNN) to establish an accurate and effective system approximation, namely an enhanced neural network module. Then, the surrogate models are generated and sent to a hybrid optimizer in which a tentative optimum result is obtained and updated into the training data to refine the response surfaces. This process, which is called Repetitively Enhanced Neural Networks (RENN), is executed repeatedly to refine the response surface until the convergent optimum solution is obtained. A numerical example and a two-member frame design are presented and discuss to demonstrate the accuracy and feasibility of RENN. Eventually, this RENN approach is applied to re-design the air intercept missile-AIM. © 2010 IEEE.

关键词： Neural networks

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A Method for Crude Oil Output Decline Rate Forecasting Based on optimization of Neural Network by GA 3

A Method for Crude Oil Output Decline Rate Forecasting Based...

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3rd International conference on Information Management (ICIM)

作者： Hu, Hongtao Hu, Xin Guan, Xin Xian Shivou Univ Sch Comp Sci Xian Shaanxi Peoples R China Res Inst Petr Explorat & Dev LangFang Langfang Hebei Peoples R China

ISBN: (纸本)9781509063062

Accurate prediction of the crude oil output decline rate is crucial to ensure the stability of oil field. This paper presents a new method that utilizes the neural networks optimized by Genetic Algorithm(GA) to dynamically predict the crude oil output decline rate. Firstly, choose the best weights for neural network by the GA's survival of the fittest mechanism. Next, learn the rules of production decrement of crude oil over the past few years by neural network which uses the Levenberg-Marquardt(LM) algorithm as training function. Lastly, test the optimized neural network with the test dataset. The experimental results show that the prediction method has reasonable weights, high learning efficiency, fast network convergence and accurate prediction result, which also provides a way for predicting other dynamic production indexes in oilfield.

关键词： crude oil output decline rate forecasting genetic algorithm BP-neural network levenberg-marquardt algorithm

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Unstructured Big Data Analysis Algorithm for Grid CPS Based on Machine learning 3

Unstructured Big Data Analysis Algorithm for Grid CPS Based ...

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3rd IEEE International conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Yue, Junying Arimuzha College of Computer Science and Technology Inner Mongolia Normal University Hohhot China

ISBN: (纸本)9798350317022

This paper delves into the realm of machine learning-based algorithms designed for the analysis of unstructured big data within Grid Cyber-Physical Systems (CPS). The study establishes a meticulous experimental framework to assess the effectiveness and accuracy of these algorithms, specifically focusing on fault detection and predictive maintenance. The results showcase a significant enhancement in the detection and forecasting of faults, contributing to an overall improvement in the operational efficiency and reliability of grid CPS. The findings hold crucial implications for advancing algorithm design, real-time analysis, and scalability to address the escalating data volumes inherent in grid CPS. Notably, the study's discoveries emphasize the potential of machine learning algorithms to elevate the intelligence and reliability of unstructured big data analysis in grid CPS. The demonstrated efficacy in fault detection and predictive maintenance sets a solid foundation for further exploration and refinement in algorithmic approaches. This research marks a noteworthy stride towards the development of more intelligent and reliable machine learning algorithms tailored for unstructured big data analysis within grid CPS. The results pave the way for smarter grid systems capable of adapting to dynamic operational challenges. The study's contributions extend beyond theoretical frameworks, providing tangible evidence of improved performance and laying the groundwork for future advancements in the intersection of machine learning and grid CPS. © 2023 IEEE.

关键词： fault detection grid CPS Machine learning unstructured big data

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