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BEESO:Multi-strategy Boosted Snake-Inspired Optimizer for Engineering Applications

Journal of Bionic Engineering 2023年第4期20卷 1791-1827页

作者： Gang Hu Rui Yang Muhammad Abbas Guo Wei Department of Applied Mathematics Xi’an University of TechnologyXi’an710054China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China Department of Mathematics University of SargodhaSargodha40100Pakistan University of North Carolina at Pembroke PembrokeNC28372USA

This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering *** a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the mating characteristics of snakes to find the optimal *** has a simple structure and offers a delicate balance between exploitation and ***,it also has some shortcomings to be *** proposed BEESO consequently aims to lighten the issues of lack of population diversity,convergence slowness,and the tendency to be stuck in local optima in *** presentation of Bi-Directional Search(BDS)is to approach the global optimal value along the direction guided by the best and the worst individuals,which makes the convergence speed *** increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics(MEPD),and the replacement of poorer quality individuals improves population *** Elite Opposition-Based Learning(EOBL)provides improved local exploitation ability of BEESO by utilizing solid solutions with good *** performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering ***,the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum *** findings show that these introduced strategies provide some improvements in the performance of SO,and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all *** conclude,the proposed BEESO offers a good alternative to solving optimization issues.

关键词： Snake optimizer Bi-Directional Search Evolutionary Population Dynamics Elite Opposition-Based Learning Strategy Mechanical optimization design

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On the Effectiveness of ViT Features as Local Semantic Descriptors 17th

On the Effectiveness of ViT Features as Local Semantic De...

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17th European Conference on computer Vision, ECCV 2022

作者： Amir, Shir Gandelsman, Yossi Bagon, Shai Dekel, Tali Department of Computer Science and Applied Mathematics The Weizmann Institute of Science Rehovot Israel Berkeley United States

ISBN: (纸本)9783031250682

We study the use of deep features extracted from a pre-trained Vision Transformer (ViT) as dense visual descriptors. We observe and empirically demonstrate that such features, when extracted from a self-supervised ViT model (DINO-ViT), exhibit several striking properties, including: (i) the features encode powerful, well-localized semantic information, at high spatial granularity, such as object parts;(ii) the encoded semantic information is shared across related, yet different object categories, and (iii) positional bias changes gradually throughout the layers. These properties allow us to design simple methods for a variety of applications, including co-segmentation, part co-segmentation and semantic correspondences. To distill the power of ViT features from convoluted design choices, we restrict ourselves to lightweight zero-shot methodologies (e.g., binning and clustering) applied directly to the features. Since our methods require no additional training nor data, they are readily applicable across a variety of domains. We show by extensive qualitative and quantitative evaluation that our simple methodologies achieve competitive results with recent state-of-the-art supervised methods, and outperform previous unsupervised methods by a large margin. Code is available in https://***/. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Zero-shot learning

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Exploring the impact of stratification on the dynamics of bioconvective thixotropic fluid conveying tiny particles and Cattaneo-Christov model:Thermal storage system application

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Propulsion and Power Research 2024年第3期13卷 416-432页

作者： T.Oreyeni M.D.Shamshuddin A.M.Obalalu A.Saeed Nehad Ali Shah Department of Physical Sciences Mathematics Programme UnitPrecious Cornerstone UniversityIbadanNigeria Department of Computer Science and Artificial Intelligence(Mathematics) SR UniversityWarangalTelangana State 506371India Department of Mathematics Augustine University Ilara EpeLagosNigeria Center of Excellence in Theoretical and Computational Science(TaCS-CoE) Faculty of ScienceKing Mongkut’s University of Technology Thonburi(KMUTT)126 Pracha Uthit Rd.BangModThung KhruBangkok 10140Thailand Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh 11623Saudi Arabia

Stratified thermal storage promotes energy sustainability by storing excess energy during times of low demand for later use making it possible to integrate renewable energy sources like solar and *** communication discusses the significance of triple stratifica-tion using the Cattaneo-Christov model in the bioconvectiveflow of thixotropicfluid coexisting with nanoparticles and gyrotactic *** Cattaneo-Christov heat and massflux is incorporated into thefluid model allowing more accurate prediction of heat and mass phenomena in thefluid *** governing partial differential equations that describefluidflow are parametrized to yield an ordinary differential equation system by adopting suitable *** series solutions are obtained by applying the homotopy analysis method(HAM).The effects of relevant parameters on the various profiles are revealed and accurately *** is observed that amplified thermal stratification lowers the temperature of ***,Brownian motion is used to illustrate the random movement of small particles suspended in liquids,and it is envisioned that the concentration distribution is significantly influenced by the Brownian motion of nanoparticles.

关键词： Cattaneo-Christov model Stratification Bioconvection Nanofluid

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Optimization Analysis of Bus Operation Scheduling Considering Passenger and Cargo Sharing under Uncertain Demand

Optimization Analysis of Bus Operation Scheduling Considerin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Jiacheng Noto, Masato Zhang, Yang Kanagawa University Department of Applied Systems and Mathematics Yokohama Japan Kanagawa University Department of Computer Science Yokohama Japan

ISBN: (纸本)9781665410205

In view of the problems faced during the ongoing development of public transport, such as departure intervals that are too long, low occupancy rates, difficulties making a profit, and high express delivery costs, bus operation and scheduling models that consider passenger and cargo sharing have been attracting attention. In this work, considering the interests of both bus companies and passengers, we propose a bus operation scheduling optimization model that ensures the largest revenue for urban and rural bus companies and the shortest travel time for passengers under uncertain demand. We also design a particle group genetic algorithm for use with this model. The results of a case study set in China demonstrate the feasibility and effectiveness of the model and algorithm. © 2024 IEEE.

关键词： Genetic algorithms

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Numerical Computation of SEIR Model for the Zika Virus Spreading

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computers, Materials & Continua 2023年第4期75卷 2155-2170页

作者： Suthep Suantai Zulqurnain Sabir Muhammad Asif Zahoor Raja Watcharaporn Cholamjiak Data Science Research Center Department of MathematicsFaculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Research Group in Mathematics and Applied Mathematics Faculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlinspan64002Taiwan School of Science University of PhayaoPhayao 56000Thailand

The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., *** correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive *** outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.

关键词： SEIR nonlinear system Zika virus artificial neural networks scaled conjugate gradient statistical measures

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Firefly Algorithm Approach for Recovering the Mathematical Structure of One-Dimensional Chaotic Maps from Time Series Data 3

Firefly Algorithm Approach for Recovering the Mathematical S...

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3rd International Conference on computer Technologies, ICCTech 2024

作者： Galvez, Akemi Fister, Iztok Fister, Iztok Iglesias, Andres University of Cantabria Dept. of Applied Mathematics & Computational Sciences Santander Spain University of Maribor Faculty of Electrical Engineering & Computer Science Maribor Slovenia

ISBN: (纸本)9798350395419

Uncovering the mathematical structure of unknown chaotic systems from limited time series data poses a significant challenge in the field of dynamical systems, with broad applications across various domains. In this paper, we propose a swarm intelligence approach to tackle this issue, specifically focusing on one-dimensional discrete maps. Our approach leverages the firefly algorithm, a popular nature-inspired metaheuristic, known for its optimization capabilities. We assess the performance of the method by applying it to two illustrative examples of time series derived from one-dimensional maps: the Hénon map and the Burger map. The results show that this approach is promising for recovering the equations of motion of one-dimensional chaotic maps, even in situations where no additional information is available beyond the provided data points. © 2024 IEEE.

关键词： Time series

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An Improved Harmony Search Algorithm for the VRPTW 24

An Improved Harmony Search Algorithm for the VRPTW

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10th International Conference on Computing and Artificial Intelligence, ICCAI 2024

作者： Zhang, Yang Li, Jiacheng Department of Computer Science Kanagawa University Yokohama Japan Department of Applied Systems and Mathematics Kanagawa University Yokohama Japan

ISBN: (纸本)9798400717055

A hybrid heuristic harmony search algorithm is proposed in this paper to solve the vehicle routing problem with time windows (VRPTW). The improved algorithm significantly improves the efficiency and accuracy of path planning through unique global and local search mechanisms and specific optimization techniques for time window constraints. We adopted the widely recognized Solomon dataset as the test benchmark and selected several representative cases for experimental verification. The experimental results are compared with the best-known solutions and comprehensively compared with the standard harmony search algorithm and other well-known variants. These comparison results show that the improved algorithm shows excellent searchability and optimization effects when dealing with diverse and complex VRPTW scenes, especially in dealing with strict time window constraints. © 2024 ACM.

关键词： Vehicle routing

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South African inflation modelling using bootstrapped long short-term memory methods

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SN Business and Economics 2023年第7期3卷 1-11页

作者： Kubheka, Sihle Department of Computer Science and Applied Mathematics University of Witswatersrand Johannesburg Campus Johannesburg 2000 South Africa

Inflation is a critical economic series, and proper targeting is required for a stable economy. With the current economic conditions that the world has faced as a result of COVID-19, understanding the effects of this on economies is critical because it will guide policies. Recent research on South African inflation has focused on statistical modelling, specifically the ARFIMA, GARCH, and GJR–GARCH models. In this study, we extend this into deep learning and use the MSE, RMSE, RSMPE, MAE, and MAPE to assess performance. To test which model has better forecasts, we use the Diebold–Mariano test. According to the findings of this study, clustered bootstrap LSTM models outperform the previously used ARFIMA–GARCH and ARFIMA–GJR–GARCH models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： ARFIMA GARCH GJR–GARCH Inflation LSTM

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Asset Management Strategies for District Heating Networks 8

Asset Management Strategies for District Heating Networks

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8th International Conference on System Reliability and Safety, ICSRS 2024

作者： Grønberg, Jens Tahavori, Maryamsadat Bacher, Peder Dtu Engineering Technology Ballerup Denmark Dtu Department of Applied Mathematics and Computer Science Kongens Lyngby Denmark

ISBN: (纸本)9798350354508

District Heating (DH) became widely adopted in European cities during the 20th century to meet demand for heating from a growing urban population. Today, the desire to decarbonize Europe's heat consumption has lead to an renewed interest in increasing the adoption of DH. To meet this end, DH has to be a price competitive alternative to other heating technologies. Over time the underground heat distribution network that DH companies operate suffers from faults that require replacement of pipes. On-demand replacement is more expensive than planned large scale replacement projects. The only question is, when is the optimal time to renew the pipes? In this article this question will be formulated using Reliability Theory. Our approach is to use historical records of time to failure and a bounding result to estimate the optimal time for replacement. The method can be used by DH companies with historical fault records to form an Asset Management strategy on the question of when to renew which pipes. © 2024 IEEE.

关键词： Asset management

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Evaluation of Various Machine Learning Algorithms for Seed Prediction Based on the Soil Nutrient Quantification 2nd

Evaluation of Various Machine Learning Algorithms for Seed P...

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Ramprakash, Balakrishnan Muthuramalingam, Sankayya Department of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai India Department of Information Technology Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9789819754113

The current condition of the soil is an important consideration in crop yield projections. An analysis of the soil’s nutrient content can help farmers and soil analysts obtain a higher yield of seedlings suited to the cultivation process by facilitating the necessary preparations. This study presents a number of machine learning techniques that have been implemented in order to predict seedlings based on soil nutrient measurements. The department of Agriculture department in South Tamil Nadu provided the data that was used in this research experimental design. This data includes soil nutrient level samples from a variety of districts located throughout the South Tamil Nadu region for a selection of districts. The evaluation of crop yields is becoming an increasingly important area of research, including machine learning. The challenge of accurately predicting yields is an extremely significant one in the agricultural industry. Any farmer worth his salt is going to want to know how much yield he may anticipate receiving from his next harvest. In the past, predictions of yield were made by factoring in the years of experience that farmers had gained working with a certain crop and field. On the basis of the data that is now available, the prediction of the yield is a significant problem that has not yet been resolved. The implementation of methods that are driven by machine learning is the approach that is going to be most successful in achieving this objective. Five supervised machine learning methods were used to analyze the study’s data: DT, NB, SVM, RF, and LR are codes for the following: Follow these ML classification techniques to plainly categorize the results. Regression is a form of supervised learning algorithm used to predict a continuous output variable given one or more predictors or features, also known as input variables. Experiments have been carried out in order to discover the method that is the most accurate in terms of seed prediction for the purpo

关键词： Adversarial machine learning

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