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Incorporating stochastics into optimal collision avoidance problems using superquadrics

Journal of Air Transportation 2020年第2期28卷 65-69页

作者： Smith, Nathan E. Cobb, Richard G. Baker, William P. Air Force Institute of Technology Wright-Patterson Air Force Base U.S. Air Force Ohio45433 United States Department of Aeronautics & Astronautics Air Force Institute of Technology Wright-Patterson Air Force Base Ohio45433 United States Department of Mathematics & Statistics Air Force Institute of Technology Wright-Patterson Air Force Base Ohio45433 United States

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Enhancing computational efficiency in topology-optimized mode converters via dynamic update rate strategies

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SCIENTIFIC REPORTS 2024年第1期14卷 1-14页

作者： Cao, Pengfei Duan, Ning Zhao, Zhikai Yu, Mengqiang Li, Congcong Yuan, Mingrui Cheng, Lin Yan, Ge Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Latitude Design Automat Inc Wuxi 214000 Peoples R China Lanzhou Univ Sch Phys Sci & Technol Lanzhou 730000 Peoples R China Lanzhou Inst Phys Lanzhou 730000 Peoples R China

In the big data era, mode division multiplexing, as a technology for extended channel capacity, demonstrates potential in enhancing parallel data processing capability. Consequently, developing a compact, high-performance mode converter through efficient design methods is an urgent requirement. However, traditional design methodologies for these converters face significant computational complexities and inefficiencies. Addressing this challenge, this paper introduces a novel topology optimization design method for mode converters employing a Dynamic Adjustment of Update Rate (DAUR). This approach markedly reduces computational overhead, accelerating the design process while ensuring high performance and compactness. As a proof-of-concept, an ultra-compact dual-mode converter was designed. The DAUR method demonstrated an 80% reduction in computational time compared to traditional methods, while maintaining a compact design (only 1.4 mu m x 1.4 mu m) and an insertion loss under 0.68 dB across a wavelength range of 1525 nm to 1575 nm. Meanwhile, simulated inter-mode crosstalk remained below - 24 dB across a 40 nm bandwidth. A comprehensive comparison with traditional inverse design algorithms is presented, demonstrating our method's superior efficiency and effectiveness. Our findings suggest that DAUR not only streamlines the design process but also facilitates exploration into more complex micro-nano photonic structures with reduced resource investment.

关键词： Mode division multiplexing Integrated optics Topology optimization Mode converter optimization algorithm Ultra compact design

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algorithm of Finding Hypo-Critical Path in Network Planning

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Physics Procedia 2012年 24卷 1520-1529页

作者： Jianxun Qi Xiuhua Zhao School of Business Administration North China Electric Power University NCEPU Beijing China

Network planning technology could be used to represent project plan management, such Critical Path Method (CPM for short) and Performance Evaluation Review Technique (PERT for short) etc. Aiming at problem that how to find hypo-critical path in network planning, firstly, properties of total float. free float and safety float are analyzed, and total float theorem is deduced on the basis of above analysis; and secondly, simple algorithm of finding the hypo-critical path is designed by using these properties of float and total theorem, and correctness of the algorithm is analyzed. Proof shows that the algorithm could realize effect of whole optimization could be realized by part optimization. Finally, one illustration is given to expatiate the algorithm.

关键词： Project management operational research network planning total float theorem optimization algorithm float

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History matching and uncertainty quantification for velocity dependent relative permeability parameters in a gas condensate reservoir

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ARABIAN JOURNAL OF GEOSCIENCES 2019年第15期12卷 454-454页

作者： Farahani, Zohreh Dermanaki Ahmadi, Mohammad Sharifi, Mohammad Amirkabir Univ Technol Hafez Ave 424 Tehran Iran

In gas condensate reservoirs, gas flow at large velocities enhances the gas permeability due to gas-liquid positive coupling which results in near-miscible flow condition. On the other hand, augmented pressure drop due to non-Darcy flow, reduces the gas permeability. Models for the Positive Coupling or non-Darcy flow include several parameters, which are rarely known from reliable lab special core analysis. We offer a good alternative for tuning of these parameters in which the observed production history data are reproduced from the readjusted simulation model. In this study, history matching on observed production data was carried out using evolutionary optimization algorithms including genetic algorithms, neighborhood algorithm, differential evolution algorithm and particle swarm optimization algorithm, where a faster convergence and lower misfit value were obtained from a genetic algorithm. Then, the Neighborhood algorithm-Bayes was used to perform Bayesian posterior inference on the history matched models and create the posterior cumulative probability distributions for all uncertain parameters. Finally, Bayesian credible intervals for production rate and wellhead pressure were computed in the long-range forecast. Our new approach enables to not only calibrate the gas effective permeability parameters to dynamic reservoir data, but allows to capture the uncertainty with parameter estimation and production forecast.

关键词： History matching Uncertainty quantification Velocity dependent relative permeability Gas condensate reservoirs optimization algorithm

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A New Discretization Approach of Bat and K-Means

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第1期12卷 510-516页

作者： Mohamed, Rozlini Samsudin, Noor Azah Univ Tun Hussein Onn Malaysia UTHM Fac Comp Sci & Informat Technol Pant Raja Johor Malaysia

Bat algorithm is one of the optimization techniques that mimic the behavior of bat. Bat algorithm is a powerful algorithm in finding the optimum feature data collection. Classification is one of the data mining tasks that useful in knowledge representation. But, the high dimensional data become the issue in the classification that interrupt classification accuracy. From the literature, feature selection and discretization able to overcome the problem. Therefore, this study aims to show Bat algorithm is potential as a discretization approach and as a feature selection to improve classification accuracy. In this paper, a new hybrid Bat-K-Mean algorithm refer as hBA is proposed to convert continuous data into discrete data called as optimize discrete dataset. Then, Bat is used as feature selection to select the optimum feature from the optimized discrete dataset in order to reduce the dimension of data. The experiment is conducted by using k-Nearest Neighbor to evaluate the effectiveness of discretization and feature selection in classification by comparing with continuous dataset without feature selection, discrete dataset without feature selection, and continuous dataset without discretization and feature selection. Also, to show Bat is potential as a discretization approach and feature selection method. The experiments were carried out using a number of benchmark datasets from the UCI machine learning repository. The results show the classification accuracy is improved with the Bat-K-Means optimized discretization and Bat optimized feature selection.

关键词： Classification discretization feature selection optimization algorithm bat algorithm

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Method of Cultivating College Students' Independent Learning Ability Based on Integration of Multiple algorithm

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第2期11卷 1-10页

作者： You, Ying Kaifeng Univ Coll Tourism Kaifeng 475004 Henan Peoples R China

INTRODUCTION: The research on the multi-mode fusion of college students' independent learning ability cultivation method is conducive to college students' change of learning mode and learning thinking, improvement of the utilization rate of educational resources, and the development of the academic environment as well as the reform of the educational concept. OBJECTIVES: Aiming at the problems of college students' current independent learning mode, such as the need for more in-depth research and the single study means. METHODS: A method for cultivating college students' autonomous learning ability through the integration of intelligent optimization algorithms and multiple modes has been proposed. Firstly, the practices of analyzing the current college students' autonomous learning mode and multiple learning modes are analyzed;then, using the butterfly optimization algorithm, a weight optimization method for the cultivation of college students' independent learning ability based on the fusion of multiple modes is proposed;finally, the validity and robustness of the proposed method are verified through experimental analysis. RESULTS: The results show that the proposed method has a high cultivation effect. CONCLUSION: Solves the problem of fusion of college students' independent learning ability cultivation modes.

关键词： college students' independent learning ability cultivation multiple modes integration blended learning model butterfly optimization algorithm

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Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

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SCIENTIFIC REPORTS 2024年第1期14卷 1-12页

作者： Wang, Haonan Wu, Danhong Luo, Jie Zhang, Junhui Southwest Med Univ Sch Publ Hlth 1Sect 1Xianglin Rd Luzhou 646000 Sichuan Peoples R China Chengdu Univ Informat Technol Dept Appl Math Chengdu 610225 Sichuan Peoples R China Univ Durham Dept Psychol Durham DH1 3LE England

The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

关键词： COVID-19 Dynamic model GA-SEIR model Epidemic forecasting Socio-psychological factors optimization algorithm

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Optimized machine learning model for air quality index prediction in major cities in India

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Natarajan, Suresh Kumar Shanmurthy, Prakash Arockiam, Daniel Balusamy, Balamurugan Selvarajan, Shitharth Jain Sch Comp Sci & Engn Bengaluru Karnataka India Presidency Univ Sch Comp Sci & Engn & Informat Sci Bengaluru Karnataka India Amity Univ ASET CSE Gwalior Madhya Pradesh India Shiv Nadar Inst Eminence Delhi India Leeds Beckett Univ Sch Built Environm Engn & Comp Leeds LS1 3HE W Yorkshire England

Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.

关键词： Air pollution Air quality index Machine learning optimization algorithm Grey-wolf optimization Decision tree regression

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Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems (vol 14, 5434, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-33页

作者： Premkumar, Manoharan Sinha, Garima Ramasamy, Manjula Devi Sahu, Santhoshini Subramanyam, Chithirala Bala Sowmya, Ravichandran Abualigah, Laith Derebew, Bizuwork Department of Electrical & Electronics Engineering Dayananda Sagar College of Engineering Kumaraswamy Layout Bengaluru Karnataka 560078 India Department of Computer Science and Engineering Jain University Ramanagaram Bengaluru Karnataka India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering GMR Institute of Technology Rajam Srikakulam Andhra Pradesh India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk 71491 Tabuk Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia 27500 Petaling Jaya Malaysia College of Engineering Yuan Ze University Taoyuan Taiwan Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Bushira Ethiopia Applied science research center Applied science private university Amman 11931 Jordan Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the gre

关键词： Computational intelligence Data mining Grey wolf optimizer K-means clustering optimization algorithm

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Time optimizing of fractal image compression using the Scatter Search algorithm

ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS

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ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS 2022年第47期47卷 365-381页

作者： Al-Bundi, Shaimaa S. Al-Saidi, Nadia M. G. Univ Baghdad Dept Math Coll Educ Pure Sci Ibn Al Ibn Al Haitham Baghdad Iraq Univ Technol Iraq Dept Appl Sci Baghdad Iraq

Image compression is one of the essential requirements for the efficient use of storage space and bandwidth. A new technique based on fractal theory is proposed for encoding the image;it is known as fractal image compression. In the procedure of encoding, the mechanism of search is considered as one of the main problems of this technique. In this work, an attempt to speed up the encoding process with minimal loss of the compressed image quality is adopted based on the Scatter Search algorithm. It is a sibling of Tabu search based on similar origins. The experimental results show a significant reduction in the computation time, where the mean square error measures be-tween blocks are decreased after comparing them to full search methods. Consequently, the decoding process evinced that the reconstructed images were of high quality.

关键词： fractal image coding (FIC) iterated function system (IFS) optimization algorithm metaheuristics scatter search method

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