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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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A Hybrid Moth Flame optimization algorithm for Global optimization

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Journal of Bionic Engineering 2022年第5期19卷 1522-1543页

作者： Saroj Kumar Sahoo Apu Kumar Saha Department of Mathematics National Institute of TechnologyAgartalaTripura 799046India

The Moth Flame optimization(MFO)algorithm shows decent performance results compared to other meta-heuristic algorithms for tackling non-linear constrained global optimization ***,it still suffers from obtaining quality solution and slow convergence *** the other hand,the butterfly optimization algorithm(BOA)is a comparatively new algorithm which is gaining its popularity due to its simplicity,but it also suffers from poor exploitation *** this study,a novel hybrid algorithm,h-MFOBOA,is introduced,which integrates BOA with the MFO algorithm to overcome the shortcomings of both the algorithms and at the same time inherit their *** performance evaluation,the proposed h-MFOBOA algorithm is applied on 23 classical benchmark functions with varied *** tested results of the proposed algorithm are compared with some well-known traditional meta-heuristic algorithms as well as MFO *** rank test and Wilcoxon signed rank test are employed to measure the performance of the newly introduced algorithm *** computational complexity has been ***,the proposed algorithm has been applied to solve one constrained and one unconstrained real-life problems to examine its problem-solving capability of both type of *** comparison results of benchmark functions,statistical analysis,real-world problems confirm that the proposed h-MFOBOA algorithm provides superior results compared to the other conventional optimization algorithms.

关键词： Moth fame optimization algorithm butterfly optimization algorithm Bio-inspired Benchmark functions Friedman rank test

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A hybrid intelligent predicting model for exploring household CO2 emissions mitigation strategies derived from butter fly optimization algorithm

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 727卷 138572-138572页

作者： Wen, Lei Cao, Yang North China Elect Power Univ Dept Econ & Management Baoding 071003 Hebei Peoples R China

The warming on earth is having a profound impact on human survival. With the improvement of people' living standard, the consumption of energy in residential sector has raised swiftly, leading to a rapid increase in corresponding CO2 emissions. To effectively mitigate household emissions, taking the Yangtze River Delta (YRD) region in China as a case study, this paper proposes a novel intelligent model combining driving forces exploration and prediction. The work first estimates the residential energy-related CO2 emissions precisely, and then the bivariate correlation analysiswill be applied to analyze region discrepancy inmain affecting factors of emissions based on 13 preliminary indicators. To obtain the principal information of above influencing factors as the input of prediction model, the kernel principal component analysis (KPCA) is introduced innovatively. Besides, butterfly optimization algorithm (BOA) is enhanced to better optimize the parameters of least square support vector machine (LSSVM). The newproposed hybridmodel, hereafter called as EBOA-LSSVM, will be utilized to predict residential CO2 emissions in the YRD. Ultimate simulation results present the new model's prominent performance through comparing prediction accuracywith othermodels. Finally, this article provides someadvice for policy makers to guide CO2 emissions reduction from residents department. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Household CO2 emissions Bivariate correlation analysis butterfly optimization algorithm Least square support vector machine

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optimization of weight and cost of cantilever retaining wall by a hybrid metaheuristic algorithm

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ENGINEERING WITH COMPUTERS 2022年第4期38卷 2897-2923页

作者： Sharma, Sushmita Saha, Apu Kumar Lohar, Geetanjali Natl Inst Technol Agartala India

A retaining wall is a structure used to resist the lateral pressure of soil or any backfill material. Cantilever retaining walls provide resistance to overturning and sliding by using backfill weight. In this paper, the weight and cost of the cantilever retaining wall have been minimized using a hybrid metaheuristic optimization technique, namely, h-BOASOS. The algorithm has been developed by the ensemble of two popular metaheuristics, butterfly optimization algorithm (BOA) and symbiosis organism search (SOS) algorithm. BOA's exploratory intensity is coupled with SOS's greater exploitative capacity to find the superior algorithm h-BOASOS. The newly developed algorithm has been tested with a suite of 35 classical benchmark functions, and the results are compared with several state-of-the-art metaheuristic algorithms. The results are evaluated statistically by the Friedman rank test, and convergence curves measure the convergence speed of the algorithm. It is observed in both cases that h-BOASOS is superior to other algorithms. The suggested approach is then used to solve four real-world engineering design problems to examine the problem-solving capacity of the proposed algorithm, and the results are contrasted with a wide range of algorithms. The proposed h-BOASOS is considered to be the winner on each occasion. Finally, the newly suggested algorithm is applied to find the cost and weight of the cantilever retaining wall problems of two different heights, 3.2 m and 6.3 m. The obtained results are compared with the component algorithms and found that the new algorithm works better than the compared algorithms.

关键词： Meta-heuristics Hybrid algorithm butterfly optimization algorithm Symbiosis organisms search Cantilever retaining wall

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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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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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Prediction optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno algorithms

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IEEE ACCESS 2020年 8卷 119911-119930页

作者： Liu, Guomin Jia, Wenyuan Luo, Yungang Wang, Mingjing Heidari, Ali Asghar Ouyang, Jinsheng Chen, Huiling Chen, Mayun Second Hosp Jilin Univ Dept Orthoped Changchun 130041 Peoples R China Second Hosp Jilin Univ Dept Stomatol Changchun 130041 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117417 Singapore Wenzhou Med Univ Affiliated Hosp 1 Dept Pulm & Crit Care Med Wenzhou 325000 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

In this research, X-ray and MRI images of patients suffering from cervical hyperextension injury are investigated. Also, radiographic images are collected from patients who suffered from trauma but without cervical hyperextension injury. The core engine algorithm of the optimized prediction model is kernel extreme learning machine (KELM), and the input data is 17 factors that may cause cervical hyperextension injury. As the optimization core, we utilized the butterfly optimization algorithm (BOA). Up to now, few improved variants of BOA have been reported. The original BOA converges slowly and quickly falls into a locally optimal solution. An enhanced BOA based on orthogonal learning, Levy flight, and an exploitation engine is proposed in this paper to relieve these two shortcomings, which is called LBOLBOA. Orthogonal learning is utilized to construct guidance vectors for guiding agents toward the global optimum solution aiming to increase the accuracy of the solutions. Also, Levy flight and Broyden-Fletcher-Goldfarb-Shanno mechanisms are utilized to enrich the intensification propensities of BOA and stagnation avoidance. The proposed LBOLBOA is used to deal with continuous function optimization and machine learning problems, including parameter optimization of KELM. We rigorously verified this variant using a comprehensive set of the benchmark test suite and real-world dataset on cervical hyperextension injury. The results indicate that LBOLBOA can achieve improved performance in dealing with the function optimization and machine learning problems, especially the capability for prediction of cervical hyperextension injury.

关键词： optimization Prediction algorithms Predictive models Injuries Kernel Support vector machines Benchmark testing butterfly optimization algorithm medical diagnosis kernel extreme learning machine parameter optimization global 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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