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3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Zhao, Dengyu Wang, Heping Chen, Mengkai Anhui Univ Technol Sch Management Sci & Engn Maanshan Anhui Peoples R China

ISBN: (纸本)9781665417907

With a view to the procurement relationship between prefabricated component (PC) production enterprise and its upstream raw material supplier, an MRP-guided collaborative optimization model for order quantity in supply chain was constructed based on the thought of just-in-time (JIT) procurement by following the BOM constraint and MRP principle of PC production. The traditional butterfly optimization algorithm (BOA) was improved by combining Levy flight strategy, mixed mutation operation and simplex method (SM), thus proposing the LMSMBOA algorithm. In a practical case, the model was solved using the improved algorithm, which was then compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm and unimproved BOA. The results reflect that the LMSMBOA algorithm can obtain more outstanding raw material procurement schemes by solving the collaborative material procurement model in supply chain between PC production enterprises and upstream raw material suppliers.

关键词： prefabricated component JIT procurement collaborative optimization butterfly optimization algorithm

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Rating Refinement and Optimized Clustering for Rating Prediction using Collaborative Filtering 24

Rating Refinement and Optimized Clustering for Rating Predic...

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6th International Conference on Big Data Engineering (BDE)

作者： Song, Wei Zhou, Jinyu North China Univ Technol Sch Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798400717857

Collaborative filtering (CF) is a typical and widely used recommendation method. The main idea is to recommend to the target user items that users similar to him/her like. CF has two main challenges. One is that users' ratings of items are sparse and the other is how to select appropriate similar users to calculate the recommendation results for target users. To address the former challenge, we use the mean ratings and median of standard ratings to fill in the missing values in the rating matrix. To address the latter challenge, we use the butterfly optimization algorithm to determine the initial centers of K-means clustering, and combine both ratings and genres of items to define the hybrid similarity of users. To evaluate the performance of the proposed method, we selected five competitive algorithms and conducted experiments on three publicly available datasets. The results showed that the proposed algorithm improved prediction accuracy.

关键词： Recommender system Collaborative filtering Rating prediction butterfly optimization algorithm Rating enrichment K-means clustering

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Adjustment Methods for Integrated Maintenance and Operation Plans of Urban Rail Trains 29

Adjustment Methods for Integrated Maintenance and Operation ...

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29th International Conference on Automation and Computing (ICAC)

作者： Li, Jiwei Zhang, Liu Jiang, Lei Zhang, Jian Southwest Jiaotong Univ Sch Mech Engn Chengdu Peoples R China

ISBN: (纸本)9798350360882;9798350360899

The common dynamic disturbances such as train breakdown and maintenance delays encountered during the actual operation of urban rail lines often lead to the inability to continue executing the original maintenance and operation plans. It is highly important to adjust maintenance and operation plans after disturbances occur while ensuring the feasibility and excellence of the new plans. In addressing this issue, an integrated adjustment model for urban rail train maintenance and operation plans is constructed in this paper, with the objectives of minimizing train maintenance costs, minimizing differential costs, maximizing train utilization balance, and maximizing train maintenance balance. Furthermore, corresponding disturbance handling strategies for two common dynamic factors, postponed maintenance tasks, and train breakdown, are designed in this paper. Then, an improved adaptive multi-objective discrete butterfly optimization algorithm is proposed in this paper to achieve integrated optimization and adjustment of maintenance and operation plans.

关键词： Urban rail trains Integrated optimization and adjustment of maintenance and operation plans Train breakdown Maintenance delays butterfly optimization algorithm

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Sentiment Data Analysis for Detecting Social Sense after COVID-19 using Hybrid optimization Method

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SN Computer Science 2023年第5期4卷 1-16页

作者： Seth, Rakhi Sharaff, Aakanksha Department of Computer Science and Engineering National Institute of Technology Raipur Chhattisgarh Raipur India

Sentiment analysis is the most effective way to understand opinions presented in digital media. Social sense is the concept of understanding every user's emotions connected through an online social media platform. This emotion helps to understand the mental health of people. Every stage of extracting social impact contains several problems with several methods associated with it, like lexicon-based techniques, which were proposed, developed, and evaluated but sometimes had poor accuracy. Machine Learning (ML) is useful to recognize patterns, in various aspects of sentiment analysis and it will best suited for this traditional algorithm where the large dataset is chunked into the smaller dataset to train the model. NLP methods do not account for different domains when modeling sentiment information. In a classification dilemma, reducing the feature set size diminishes the algorithm's time demand while improving the method's accuracy to obtain the optimal features. Scalability refers to handling large amounts of data and performing several computations like time and cost efficiently. This paper's perspective is to overcome the shortcomings of each system, and this article proposes a hybrid approach to sentiment analysis. Data has been collected from Twitter via the Twitter API. This research investigates the effects of negations, URLs, usernames, punctuation, repeated character normalization, and hashtags on sentiment classification output using an n-gram representation model, which is a kind of probabilistic language model and also integrates a cross-domain sentiment-aware learning model that can collect both sentiment awareness and domain validity of a word simultaneously to solve the problem of words from various domains. To fix the scalability problem, the article offers the novel Tunicate Swarm algorithm (TSA), which increases scalability and reduces computation time. Meanwhile, this paper custom a hybrid Harris Hawks optimization (HHO) algorithm based on simulat

关键词： ANN classification butterfly optimization algorithm Feature selection N-gram representation ROC curve Sentiment analysis Simulated annealing Tunicate swarm algorithm Twitter Word embedding

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Refining Machine Learning for Employee Turnover Classification: A CAP-BOA-Based Probability Neural Network Approach 3

Refining Machine Learning for Employee Turnover Classificati...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Liu, Yan Lyceum of the Philippines University Manila Claro M. Recto Academy of Advanced Studies Manila Philippines

ISBN: (纸本)9798350380989

This paper introduces a novel approach, the CAP-BOA-based Probabilistic Neural Network (PNN), aimed at optimizing the smoothing factor of PNN to address the suboptimal performance issues encountered by traditional methods in handling complex classification problems. The CAP-BOA algorithm leverages the global and local search capabilities of the butterfly optimization algorithm (BOA) and integrates Cauchy mutation, adaptive weight optimization, and dynamic switching probability strategy to enhance the rationality of computation and switching strategies during the optimization process. Compared to conventional PNN optimization methods, the CAP-BOA-based PNN demonstrates a significant improvement in convergence accuracy. Experimental results validate the efficacy of this approach in overcoming traditional limitations, particularly in the scenario of classifying employee turnover within enterprises. The findings showcase the effectiveness of the CAP-BOA-based PNN in achieving enhanced overall convergence accuracy. © 2024 IEEE.

关键词： Alogrithm optimization Techniques butterfly optimization algorithm Cauchy mutation Probabilistic Neural Network

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Apple Fruit leaf Disease Detection and Classification using Quantum Behaved Particle Swarm optimization 2

Apple Fruit leaf Disease Detection and Classification using ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Math, Gayatri Vishwanath, P. Adnan, Myasar Mundher Reddy, R Archana Sundaram, S. Meenakshi Raichur Visvesvaraya Technological University H.K.E. Society's S.M.V. College of Engineering Department of ECE Karnataka Belgaum India H.K.E. Society's S.M.V. College of Engineering Department of ECE Karnataka Raichur India The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Nitte Meenakshi Institute of Technology Department of AI and DS Bengaluru India

ISBN: (纸本)9798350317558

Detecting and classifying apple fruit leaf diseases manually is a time-consuming and expensive task, requiring expertise. This process not only affects the quality of the leaf disease image but also demands expert intervention. In the dataset, there are 1615 healthy images distributed among four classes: apple scab, apple rust, apple black rot, and healthy apple. Data augmentation techniques were involved image inversion, noise injection, colour enhancement, rotation and scaling. Despite these efforts, the existing model faces challenges in an inefficient search space with high dimensions and local optima. To enhance the Quantum-Behaved Particle Swarm optimization (QBPSO) based Long Term-Short Memory (LSTM) dimension the search space is expanded globally, independently detecting the leaf disease. Feature extraction is performed using Squeeze Net layers to analyse the leaf disease, reducing parameters to improve the detection process. The fire modulus in the analysis is involved in the process. The hyperparameter tuning process utilizes QBPSO to independently detect and classify the leaf disease, with the Schrödinger equation playing a crucial role in the process. The obtained results demonstrate that the proposed method achieves better accuracy of 98.76%, Precision of 98.72%, Sensitivity of 98.82% and F1-score of 98.70%. on the plant dataset. These results ensure detection and classification performance compared to other existing methods, such as Discrete Wavelet Transformation (DWT), butterfly optimization algorithm (BOA) and Chaotic Salp Swarm algorithm (CSSA). © 2024 IEEE.

关键词： Apple Leaf Disease butterfly optimization algorithm Chaotic Salp Swarm algorithm Long Term-Shor Memory and Quantum-Behaved Particle Swarm optimization

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optimization of Model Predictive Control for Autonomous Vehicles Through Learning-Based Weight Adjustment

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年

作者： Li, Haoran Lu, Yunpeng Li, Yaqiu Zheng, Sifa Sun, Chuan Zhang, Junyi Liu, Liqun Wuhan Univ Sci & Technol Sch Automobile & Traff Engn Wuhan 430065 Peoples R China Tsinghua Univ Suzhou Automot Res Inst Suzhou 215134 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima 7390046 Japan Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Univ Leeds Sch Comp Leeds LS2 9BW England

Model Predictive Control (MPC) method is widely used in autonomous vehicle control technology. The adjustment of MPC weights is crucial for optimizing its control performance, ensuring precise and reliable operation. Traditionally, these weights are adjusted manually, which is inefficient. This study introduces a novel butterfly optimization algorithm (BOA) learning-based method to determine the optimal MPC weights in an efficient way. By adopting the data-driven idea in machine learning, the trajectory data of field experiment human drivers is used to train the controller weights. A simulation-based training platform that enables the automatic training of the MPC controller with varying weights is also developed. Simulation results demonstrate the superior control accuracy and stability performance of BOA learning-based method compared to Linear Quadratic Regulator (LQR) and pure pursuit strategies. The findings suggest that the control method proposed in this research can significantly improve autonomous vehicle control performance and their reliability, thereby contributing to the advancement of autonomous driving technology.

关键词： optimization Autonomous vehicles Mathematical models Vehicle dynamics Accuracy Predictive models Machine learning algorithms Model predictive control autonomous vehicle butterfly optimization algorithm machine learning control theory

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Fault Detection of Wind Turbine Gearboxes Based on IBOA-ERF

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SENSORS 2022年第18期22卷 6826-6826页

作者： Tang, Mingzhu Cao, Chenhuan Wu, Huawei Zhu, Hongqiu Tang, Jun Peng, Zhonghui Wang, Yifan Changsha Univ Sci & Technol Sch Energy & Power Engn Changsha 410114 Peoples R China Hubei Univ Arts & Sci Hubei Key Lab Power Syst Design & Test Elect Vehi Xiangyang 441053 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty of optimizing the parameters of wind turbine gearbox fault detection models based on extreme random forest, a fault detection model with extreme random forest optimized by the improved butterfly optimization algorithm (IBOA-ERF) is proposed. The algebraic sum of the false alarm rate and the missing alarm rate of the fault detection model is constructed as the fitness function, and the initial position and position update strategy of the individual are improved. A chaotic mapping strategy is introduced to replace the original population initialization method to enhance the randomness of the initial population distribution. An adaptive inertia weight factor is proposed, combined with the landmark operator of the pigeon swarm optimization algorithm to update the population position iteration equation to speed up the convergence speed and improve the diversity and robustness of the butterfly optimization algorithm. The dynamic switching method of local and global search stages is adopted to achieve dynamic balance between global exploration and local search, and to avoid falling into local optima. The ERF fault detection model is trained, and the improved butterfly optimization algorithm is used to obtain optimal parameters to achieve fast response of the proposed model with good robustness and generalization under high-dimensional data. The experimental results show that, compared with other optimization algorithms, the proposed fault detection method of wind turbine gearboxes has a lower false alarm rate and missing alarm rate.

关键词： fault detection butterfly optimization algorithm extreme random forest wind turbine gearbox

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Near-Ground Delivery Drones Path Planning Design Based on BOA-TSAR algorithm

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DRONES 2022年第12期6卷 393-393页

作者： Luo, Yuan Lu, Jiakai Zhang, Yi Zheng, Kai Qin, Qiong He, Lin Liu, Yanyu Chongqing Univ Posts & Telecommun Key Lab Optoelect Informat Sensing & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China

With the advancement of technology and the rise of the unmanned aerial vehicle industry, the use of drones has grown tremendously. For drones performing near-ground delivery missions, the problem of 3D space-based path planning is particularly important in the autonomous navigation of drones in complex spaces. Therefore, an improved butterfly optimization (BOA-TSAR) algorithm is proposed in this paper to achieve the autonomous pathfinding of drones in 3D space. First, this paper improves the randomness strategy of the initial population generation in the butterfly optimization algorithm (BOA) via the Tent chaotic mapping method, by means of the removal of the short-period property, which balances the equilibrium of the initial solutions generated by the BOA algorithm in the solution space. Secondly, this paper improves the shortcomings of the BOA algorithm in terms of slower convergence, lower accuracy, and the existence of local optimal stagnation when dealing with high-dimensional complex functions via adaptive nonlinear inertia weights, a simulated annealing strategy, and stochasticity mutation with global adaptive features. Finally, this paper proposes an initial population generation strategy, based on the 3D line of sight (LOS) detection method, to further reduce the generation of path interruption points while ensuring the diversity of feasible solutions generated by the BOA algorithm for paths. In this paper, we verify the superior performance of BOA-TSAR by means of simulation experiments. The simulation results show that BOA-TSAR is very competitive among swarm intelligence (SI) algorithms of the same type. At the same time, the BOA-TSAR algorithm achieves the optimal path length measure and smoothness measure in the path-planning experiment.

关键词： drones butterfly optimization algorithm path planning tent chaotic mapping adaptive nonlinear inertia weights simulated annealing adaptive random mutation

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Container throughput forecasting using a novel hybrid learning method with error correction strategy

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KNOWLEDGE-BASED SYSTEMS 2019年 182卷 104853-000页

作者： Du, Pei Wang, Jianzhou Yang, Wendong Niu, Tong Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Port throughput forecasting is not only a complicated problem but also a challenging task in port management fields. In this study, a novel hybrid learning model that utilizes effective decomposition techniques, such as variational mode decomposition (VMD), machine learning, optimization algorithms, and error correction strategies, is developed for container throughput forecasting. First, VMD is adopted to divide the original data into a finite set of components: subsequently, the different features hidden in the container throughput time series can be extracted by different modes from low frequency to high frequency. Next, each component obtained from VMD is modeled and predicted by an extreme learning machine (ELM) technique optimized by a butterfly optimization algorithm (BELM): subsequently, another BELM predictive model based on a training error series is constructed to predict the consequent error. Next, the correction of preliminary prediction values is calibrated. Finally, hypothesis testing, six model evaluation criteria, eleven comparison models, and two case studies are utilized to comprehensively evaluate the developed hybrid method. Based on the experimental results and related analysis, it can be fund that the proposed hybrid method is superior to the eleven comparison models in terms of prediction accuracy, and can be regarded as an effective tool for port operation management. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Container throughput forecasting butterfly optimization algorithm Hybrid forecasting model Error correction strategy

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