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检索条件"任意字段=2016 International Congress on Computation Algorithms in Engineering"

共 1728 条记录，以下是91-100 订阅

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A Bilevel Hybrid Genetic Algorithm for Capacitated Electric Vehicle Routing Problem 13

A Bilevel Hybrid Genetic Algorithm for Capacitated Electric ...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Feng, Chang-Tao Jia, Ya-Hui Yang, Qiang Chen, Wei-Neng Jiang, Huaiguang School of Future Technology South China University of Technology Guangzhou China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350308365

As electric vehicles become more prevalent, a novel vehicle routing problem (VRP) has emerged, known as the capacitated electric VRP (CEVRP). CEVRP requires determining not only the service order of customers but also the charging plans for vehicles, thereby increasing the complexity of solution construction. In response to this challenge, we propose a bilevel hybrid genetic algorithm (BHGA). BHGA models CEVRP as two levels of subproblem: 1) the upper level capacitated VRP, focusing on the service order and 2) the lower level fixed route vehicle charging problem, focusing on the charging plans. In dealing with the upper level subproblem, the hybrid genetic search algorithm is adopted to construct the routes to visit customers and an advanced screening strategy is proposed to optimize the local search process and effectively guide the evolution of population. For the lower level subproblem, an efficient heuristic method called focus enumeration is designed, which is specifically used to insert charging stations into routes to ensure battery constraint. The collaboration of the advanced screening strategy and the focus enumeration assists in more unified solving of the two subproblems. The experiments show that BHGA significantly surpasses state-of-the-art algorithms on benchmark instances and has successfully updated eleven best known solutions, demonstrating its outstanding performance. © 2024 IEEE.

关键词： Heuristic methods

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Two-Level Software Obfuscation with Cooperative Co-Evolutionary algorithms 13

Two-Level Software Obfuscation with Cooperative Co-Evolution...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Aragón-Jurado, José Miguel Jareño, Javier De La Torre, Juan Carlos Ruiz, Patricia Dorronsoro, Bernabé Higher School of Engineering University of Cadiz Cadiz Spain

ISBN: (纸本)9798350308365

Computing devices are ubiquitous nowadays and because of the rise of new paradigms as the Internet of Things, their presence is continuously growing. Software (SW) is highly exposed, and SW companies are forced to protect their products from attacks to prevent plagiarism and the detection of security flaws. Obfuscation is a widespread technique to protect SW. It consists in making the code unintelligible, so that it is very hard to learn how it works. There are numerous obfuscation techniques, but they often require expert hands. Therefore, there is a clear need for fully automatic obfuscation tools that can offer high quality outputs independently of the specific features of the considered SW. In this work, we define a novel combinatorial optimization problem for a two-level obfuscation method that makes use of typical obfuscation transformations, those provided by Tigress framework, as well as classical optimization ones, those from LLVM compilation framework. The problem is solved with a cooperative co-evolutionary cellular genetic algorithm, providing a tool for automatic SW obfuscation. Three different obfuscation metrics are considered as fitness function. The results show that the proposed methodology offers outstanding obfuscation results, outperforming the original programs by up to 6,152,547%. Moreover, compared to approaches from the literature, these results are as much as 405 times better. © 2024 IEEE.

关键词： Combinatorial optimization

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Harnessing Machine Learning for Reliable Weather Forecasting: Meteorological Impact on Sustainable Energy in Monterrey 13

Harnessing Machine Learning for Reliable Weather Forecasting...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： De Jesús MacHado-Guillén, Gustavo Cruz-Duarte, Jorge M. Conant-Pablos, Santiago E. Filus, Katarzyna School of Engineering and Science Tecnologico de Monterrey Monterrey Mexico Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland

ISBN: (纸本)9798350308365

In this study, we assess the performance of var-ious machine learning algorithms in weather forecasting for Monterrey, Mexico, a large metropolitan area with a mountain-influenced semi-arid climate whose geographical features significantly hinder weather prediction. To tackle this problem, we propose using heterogeneous models: Random Forest Regression, Support Vector Regression, Gradient Boosting Regression, and Long Short-Term Memory neural networks. Our research targets seven crucial weather parameters and utilizes lagged feature sets and an iterative prediction model to implement these ML models for forecasting weather conditions several days in advance. We compare the performance of our models via several metrics and statistical tests. Our findings reveal unexpected patterns, such as improved model accuracy over extended forecast periods. However, we find promising results for establishing the basis for future work on weather forecasting in mountain-influenced semi-arid climates. © 2024 IEEE.

关键词： Prediction models

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Lightweight CNN Frameworks and their Optimization using Evolutionary algorithms

Lightweight CNN Frameworks and their Optimization using Evol...

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2022 international Electrical engineering congress, iEECON 2022

作者： Chang, Yangyang Sobelman, Gerald E. University of Minnesota Dept. of Electrical and Computer Engineering MinneapolisMN United States

ISBN: (纸本)9781665402064

This paper presents novel frameworks for efficient and lightweight convolutional neural networks that are suitable for use in embedded system applications. Population-based metaheuristic approaches including the genetic algorithm, cuckoo search, the multifactorial evolutionary algorithm and a proposed hybrid approach are used to optimize their performance on image classification tasks. The methods utilize small population sizes without requiring weight-sharing or a surrogate function, and both binary and integer encoding methods are applied in the optimization. The results from these various strategies are evaluated using metrics of computation time and classification accuracy. The multifactorial-based approach is found to give the highest classification accuracy and it requires only a moderate evaluation time. Also, comparisons with prior approaches demonstrate that these methods show a favorable tradeoff between accuracy and computational cost. © 2022 IEEE.

关键词： Network architecture

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Comparison of Unsupervised Learning algorithms for K-Means Clustering Efficiency in IoE Sensor Detection Systems for Smart Agriculture

Comparison of Unsupervised Learning Algorithms for K-Means C...

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international Electrical engineering congress (iEECON)

作者： Wallop Tipwong Adisorn Sirikham Jessada Konpang Putthiphong Kirdpipat Yutana Chongjarearn Oradee Preutisrunyanont Department of Electrical and Telecommunication Engineering Rajamangala University of Technology Krungthep Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Thammasat School of Engineering Thammasat University Pathum Thani Thailand Department of Robotic Engineering and Automation System Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This study investigates the application of clustering algorithms to data collected from color sensors providing frequency signals during the cultivation process. Continuous sampling allowed for real-time data collection, and variations in temperature and humidity were observed to influence signal reception. The dataset's large volume and varying sample sizes necessitated the evaluation of several clustering models for efficient data processing. The experiment tested K-Means, K-Means++, Mini-Batch K-Means, and Bisecting K-Means algorithms. The results reveal that the computation time for each model increased with the number of groups. Specifically, the K-Means, K-Means++, and Mini-Batch K-Means models exhibited shorter computation times, ranging from 0.02 to 0.03 seconds for two-group scenarios, while the Bisecting K-Means algorithm took significantly longer, approximately 0.10 seconds. Despite the variation in processing times, all models performed similarly in terms of clustering quality, with an average Silhouette Score of 0.82. This score indicates that the algorithms effectively separated data points into well-defined groups while maintaining high accuracy in clustering. Additionally, the analysis highlighted the importance of balancing computation time and clustering quality, particularly as the number of clusters increases. These findings suggest that while computational efficiency may vary across models, the clustering quality remains robust, providing valuable insights for data analysis in agricultural sensor networks. The study's results demonstrate that each clustering model is capable of handling large datasets with high accuracy in grouping, making them applicable to real-world agricultural data processing scenarios. In conclusion, the experiment confirms that all tested models, despite differing in computation time, successfully partitioned the data with high-quality results, offering promising applications for data analysis and optimization in agricult

关键词： Temperature sensors Analytical models Image color analysis computational modeling Clustering algorithms Approximation algorithms Data models Sensor systems computational efficiency Optical sensors

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A Proposal for Creating Congruent Content of Different Media Types by Interactive Evolutionary computation 12

A Proposal for Creating Congruent Content of Different Media...

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12th international congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Fukumoto, Makoto Miyamoto, Taichi Gan, Haoran Fukuoka Institute of Technology Dept. of Computer Science and Engineering Fukuoka Japan Fukuoka Institute of Technology Graduate School of Computer Science and Engineering Fukuoka Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

We enjoy multiple media content in our daily lives, and using congruent content composed of different media types is ideal. However, obtaining congruent media content is difficult. Interactive Evolutionary computation (IEC) is known as an effective approach to obtain good solutions for each user. However, conventional IECs treat single media types. This study proposes new Interactive Evolutionary computation that searches the congruent media content in terms of a good combination of different types of media content. In the proposed IEC, the solution candidate contains different media types as its variables. A system was constructed by employing a genetic algorithm to investigate the effectiveness of the IEC with experiment. The target of creation was the congruent content composed of music and scent. Twelve participants evaluated a set of musical melodies and scents repeatedly. As results, a trend of increase in the mean fitness and significant increase in maximum fitness were observed in the search experiment. However, enough increase in the fitness was not observed in the evaluation experiment. © 2022 IEEE.

关键词： Genetic algorithms

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Can Big Population Always Bring Better Optimization Ability to Evolutionary computation for Large-Scale Optimization?

Can Big Population Always Bring Better Optimization Ability ...

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2022 IEEE international Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Jian, Jun-Rong Chen, Chun-Hua Liu, Dong Zhang, Jun Zhan, Zhi-Hui South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Guangzhou510006 China South China University of Technology School of Software Engineering Guangzhou510006 China Henan Normal University School of Computer and Information Engineering Xinxiang453007 China Hanyang University Ansan15588 Korea Republic of

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Artificial intelligence (AI) has fast developed nowadays especially in the deep learning field that most of the deep neural networks pursue the better problem-solving ability by making the network models deeper, larger, and more complex. However, too large and too complex AI algorithms/models require too large computational burden, which is not reality in academic researches nor the right way to real human intelligence. Moreover, in another research branch of AI named evolutionary computation (EC), which is inspired by the biological evolution of nature and swarm intelligence behaviours, will the EC algorithms become more efficient with larger and more complex algorithm model when solving complicated optimization problems like the large-scale optimization problems (LSOPs)? To this concern, this paper investigates whether some existing large-scale optimization EC algorithms can further improve their performance in solving LSOPs by only increasing the population size. We select 12 representative algorithms for investigation, including 4 standard EC algorithms and 8 well-known large-scale optimization algorithms. Then, we adopt the widely-used IEEE congress on Evolutionary computation (CEC 2010) LSOPs benchmark test suite to compare the performance of the same algorithms with different population sizes. The experimental results show that simply increasing the population size does not necessarily improve the performance of algorithms in solving LSOPs. © 2022 IEEE.

关键词： Evolutionary algorithms

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Multi-Agent Collaborative Search with Adaptive Heuristics and Weight Vectors for Aerospace Multi-objective Optimal Control Problems 13

Multi-Agent Collaborative Search with Adaptive Heuristics an...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Cao, Li Wang, Maocai Parsonage, Ben Maddock, Christie Dai, Guangming China University of Geosciences School of Computer China China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430074 China University of Strathclyde Department of Mechanical & Aerospace Engineering Glasgow United Kingdom

ISBN: (纸本)9798350308365

Optimal control optimization problems are relative complex and widespread in aerospace field. The evolutionary algorithms are useful methods in solving those problems and Multi Agent Collaborative Search Algorithm (MACS) is a effective method for them. It's a mix of evolution approach and gradient-based method. The evolution part includes individualistic and social heuristics with different characteristics. The latest version of MACS is Multi-Agent Collaborative Search optimal control algorithm (MACSoc) which has good performance on multi-objective optimal control problems. However, this algorithm has two important limitations: The two kinds of heuristics are performed evenly on each individuals which may weaken the algorithm efficiency;The Pareto Front in objective space may be uneven but the initial weight vectors of MACSoc is even. This may lead to diversity losing. We proposed a new algorithm MACSoc with adaptive heuristics and weight vectors (MACSoc-AH) which contains the following improvements: The two kinds of heuristics are executed on individuals based on the their characteristics;A weight vector adjusting process with a original trigger is added to improve diversity. The new algorithm is compared with MACSoc and shows competitive results. © 2024 IEEE.

关键词： Aerospace industry

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Deep Learning Based Nonlinear Dimensionality Reduction for Emulators of Numerical Thermosphere Density Models 13

Deep Learning Based Nonlinear Dimensionality Reduction for E...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Licata, Richard J. Mehta, Piyush M. Mechanical and Aerospace Engineering West Virginia University Morgantown United States

ISBN: (纸本)9798350308365

Modeling and forecasting of atmospheric drag for space objects in low Earth orbit (LEO) is a critical challenge for space situational awareness and environment safety and sustainability. The largest source of dynamics error or uncertainty affecting drag is the thermospheric density. Current operations use empirical models that lack fidelity and are deterministic resulting in unrealistic state and covariance vrel predictions. For more than two decades, numerical physics-based models of thermospheric density have been touted as the next big thing for drag modeling. However, the computational cost combined with the lack of mature algorithms for data assimilation (or CD model-data fusion) has not yet allowed them to make impact in operations. The research community has seen a recent trend towards the development of reduced order models or emulators to overcome these limitations for enabling operational deployment of numerical density models. Dimensionality reduction is an important first step in this process. We build upon previous work in the community to design a nonlinear dimensionality reduction approach based in a deep convolutional autoencoder (CAE). We develop a new architecture that employs an attention module, spatial loss scaling, and weighted sampling to optimize performance and overcome data imbalance. We also employ an orthogonality constraint for enabling robust data assimilation as the next step. Results show that we achieve performance similar in terms of reconstruction error (-2%) to that of the robust but linear principal component analysis (PCA) approach while significantly improving performance during nonlinear periods of geomagnetic storms. © 2024 IEEE.

关键词： Orbits

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Crime Mapping and Predictive Analysis of Crimes in Maryland, USA

Crime Mapping and Predictive Analysis of Crimes in Maryland,...

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2024 international Conference on Signal Processing, computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Thomas, Beryl Abey Raja, Sivakami School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

The issue of crime has long existed, both nationally and internationally. Several research have looked at previous crimes and provided appropriate preventive measures. However, even in recent years, there has been a high prevalence of crime. Therefore, the primary objective of this study is to analyze the crimes that took place in Maryland, USA, between 2016 and 2020. After pre-processing and cleaning the dataset, numerous analysis and visualization findings were drawn. Improving public safety and crime prevention in the state of Maryland requires first developing a predictive analytic and crime mapping technology. By examining past trends and data, it was possible to ascertain the days of the week, time of the day, and months of the year that crimes were most likely to occur. In addition, the location of these occurrences and the most frequent form of crime each year could be inferred. The three primary machine learning algorithms that were employed to predict the kind of crime that would occur given other factors were K Neighbors Classifier, Random Forest Classifier, and Logistic Regression. The accuracy was moderate to low. The Random Forest Classifier, Logistic Regression, and K Neighbors Classifier displayed an accuracy of 52.55%, 43.86% and 38.39%. Nevertheless, by displaying past crime data trends, law enforcement officials can identify crime hotspots, identify emerging trends, and allocate resources efficiently. This would enable law enforcements to anticipate and prevent crimes before they occur, allowing for proactive interventions. © 2024 IEEE.

关键词： Crime

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