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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Ahmad, Dzaky Raihan Jondri Kurniawan, Isman School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350372229

Drug side effects are an unwanted response by the body. This side effect becomes a serious problem for health and can cause effects such as nausea, headaches, and even death. Currently, the detection of side effects still uses clinical trials that are time-consuming and costly. In fact, it occurs often that possible side effects go unnoticed due to the limitations of the tested samples. The alternative is new, effective, and efficient detection, namely machine learning. However, one of the challenges in drug detection using machine learning is the high dimension of features, so the hybrid bat algorithm (HBA) for feature selection can be applied to this. This study aims to predict drug side effects with a case study of metabolism and nutrition disorders by using the hybrid bat algorithm-ensemble method. In this study, the feature selection process utilized the hybrid bat algorithm, and an ensemble approach was employed to construct the prediction model using three techniques: Random Forest, AdaBoost, and XGBoost. Based on the experiments that have been carried out, the model created is successful in predicting drug side effects. XGBoost produces the most accurate model with the best performance, with accuracy metrics and F1-Score values of 0.743 and 0.811, respectively. © 2024 IEEE.

关键词： Drug Side Effect Ensemble hybrid bat algorithm Machine Learning

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Implementation of hybrid bat algorithm-Ensemble on Human Oral Bioavailability Prediction of Drug Candidate

Implementation of Hybrid Bat Algorithm-Ensemble on Human Ora...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Wirasantoso, Muhamad Farhan Hasmawati Kurniawan, Isman School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350372229

One of significant parameters of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) is Human Oral Bioavailability (HOB) which is crucial for determining the total of consumed drugs inside humans body circulation. Poor HOB results in undeterminable drug effects in the human body, with approximately 50% of drug candidates failing due to low oral availability. As many as 80% of drugs in the world use the oral route of entry into the body, so HOB prediction is very important to reduce side effects and the risk of toxicity brought by drugs. Unfortunately, oral bioavailability is currently predominantly measured in vivo consequently, developing in-silico methods is considered crucial. To reckon the human oral bioavailability of medication candidates, we used the hybrid bat algorithm method for feature selection and the Ensemble method, i.e. Random Forest, AdaBoost, and XGBoost for the prediction model. The result showed that XGBoost as the best model in which the value of accuracy and Fl-score were 0.776, and 0.802, respectively. © 2024 IEEE.

关键词： Absorption Distribution Metabolism Excretion Toxicity (ADMET) Drugs Ensemble Human Oral Bioavailability (HOB) hybrid bat algorithm Machine Learning

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A Novel Support Vector Machine Based on hybrid bat algorithm and Its Application to Identification of Low Velocity Impact Areas

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IEEE ACCESS 2020年 8卷 8286-8299页

作者： Liu, Qi Wu, Lei Wang, Fengde Xiao, Wensheng China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Peoples R China Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266590 Peoples R China

The standard support vector machine (SVM) performs poorly on the identification problem of low velocity impact areas due to its lower accuracy rate. Improving SVM2019;s performance using the bat algorithm (BA) is feasible, but BA has the premature convergence problem. In this study, a hybrid bat algorithm with double mutation operations (DMBA), in which the Cauchy mutation operator and the extremal optimization mutation operator are integrated into BA, is proposed to enhance BA2019;s ability to jump out of the local optima. Then, a novel SVM based on this hybrid BA, which is called SVM005F;DMBA, is developed to address the identification problem. Compared with the standard SVM and twelve improved SVM methods which are combined with the standard algorithms, advanced algorithms, and bat variants, the significant performance of SVM005F;DMBA is validated using UCI datasets. Moreover, to identify low velocity impact areas, SVM005F;DMBA is applied to the low velocity impact localization system based on fiber Bragg grating (FBG) sensors. The statistical results indicate that SVM005F;DMBA is a significantly effective method for identifying the low velocity impact areas and generates higher identification accuracy than comparative methods. For 64 low velocity impact areas of on an aluminium plate, the average identification error obtained by SVM_DMBA is 1.615%.

关键词： Extremal optimization fiber Bragg grating sensor hybrid bat algorithm low velocity impact areas support vector machine

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Dynamic versus static rebates: an investigation on price, displayed stock level, and rebate-induced demand using a hybrid bat algorithm

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ANNALS OF OPERATIONS RESEARCH 2019年第1-2期279卷 187-219页

作者： Dey, Kartick Chatterjee, Debajyoti Saha, Subrata Moon, Ilkyeong Univ Engn & Management Dept Math Kolkata India Indian Inst Informat Technol & Management IIITM K Thiruvananthapuram Kerala India Aalborg Univ Dept Mat & Prod DK-9220 Aalborg Denmark Seoul Natl Univ Dept Ind Engn Seoul 08826 South Korea Inst Ind Syst Innovat Seoul 08826 South Korea

Joint determination of price, rebate, investment in preservation technology, and order quantity is a complex task for retailers today. To help retailers, this paper presents an investigation on a replenishment policy for deteriorating products that focused on the choice between dynamic and static rebates under the price, displayed stock level, and rebate-induced demand. With the objective of maximizing the retailer's profit, six different models were formulated under static and dynamic environments to identify optimal price-and-rebate pair and preservation technology investment policy. Optimal control theory was employed to determine the rate of dynamic rebate. A hybrid bat algorithm (HBA) is developed to find solutions for the proposed non-linear optimization problems. The efficiency of the proposed algorithm was verified with standard test functions. Price sensitivity, the nature of the product, and display stock elasticity were found to be decisive parameters for a retailer's rebate strategy. Dynamic rebate on initial price of the product can significantly improve the profit of the retailer. The retailer's investment decision was also significantly influenced by the nature of the product. Sensitivity analyses were carried out to offer managerial insights.

关键词： Rebate Inventory Optimal control hybrid bat algorithm

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Optimizing a bi-objective inventory model of a three-echelon supply chain using a tuned hybrid bat algorithm

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2014年第1期70卷 274-292页

作者： Sadeghi, Javad Mousavi, Seyed Mohsen Niaki, Seyed Taghi Akhavan Sadeghi, Saeid Islamic Azad Univ Young Researchers & Elite Club Qazvin Iran Sharif Univ Technol Dept Ind Engn Tehran Iran Islamic Azad Univ Young Researchers & Elite Club South Tehran Branch Tehran Iran

This paper presents a bi-objective VMI problem in a single manufacturer-single vendor multi-retailer (SM-SV-MR) supply chain, which a redundancy allocation problem is incorporated. In the hybridized problem, a manufacturer produces a single item using several machines that work in series, and stores it in a warehouse to replenish one vendor who delivers it to several retailers using the shortest possible route. A novel meta-heuristic, called hybrid bat algorithm (HBA), with calibrated parameters is utilized to find a near-optimum solution. To show the efficiency of HBA, the results are compared to the ones using the traditional BA and a genetic algorithm. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Vendor managed inventory (VMI) Transportation cost Redundancy allocation problem (RAP) hybrid bat algorithm Parameter tuning

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Optimization Method for Distribution Network Investment Projects on the Basis of Brittle Link Entropy and Cumulative Prospect Theory

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Journal of Engineering Science and Technology Review 2024年第6期17卷 187-198页

作者： Wen, Jianzhou Wang, Huiyang Li, Xiaoxu Zhang, Gelin Cheng, Fen State Grid Shanxi Electric Power Company Yuncheng Power Supply Company Yuncheng044000 China

An optimization method for distribution network investment projects on the basis of brittle link entropy and cumulative prospect theory was proposed to address the problem of single time scale and insufficient consideration of the correlation between projects in traditional distribution network investment. First, brittle link entropy was applied to analyze the correlation between projects and calculate the project implementation risks. Second, projects were divided into three levels in accordance with their implementation purposes, and the relationship between them and the distribution network indexes was established to determine the contributions of the distribution network projects to the power grid. On this basis, an evaluation index system for projects was created. Last, the projects were comprehensively evaluated using cumulative prospect theory containing the time discount function, and the scores of all the projects were given based on the project implementation risks and prospect values. With the total score of the project group being the highest, an investment optimization model was established, and the optimal investment scheme was obtained by the hybrid bat algorithm. Analyses of numerous examples show that the risks of project investment can be measured by brittle link entropy, and the necessity of project implementation can be scored using the project evaluation method based on cumulative prospect theory containing the time discount function. © (2024), (International Hellenic University School of Science and Technology). All rights reserved.

关键词： Brittle link entropy Cumulative prospect theory Distribution network investment hybrid bat algorithm Time discount function

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A novel optimization method for geological drilling vertical well

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INFORMATION SCIENCES 2023年 634卷 550-563页

作者： Zhou, Yang Chen, Xin Wu, Min Cao, Weihua China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

Geological drilling is an important means for exploration of the earth. Due to the nonlinearity and coupling, geological drilling is accompanied by low efficiency and safety, and difficult to accurately predict the drilling states. Moreover, the vertical well trajectory needs to follow the plumb line. A novel optimization method is proposed to solve these problems. First, working conditions are identified by fuzzy C-means clustering method and corresponding adjustment ranges of operating variables are refined for optimization. Meanwhile, support vector regression and hybrid bat algorithm are employed to construct reliable models for mud pit volume (MPV) and rate of penetration (ROP). Then, how to improve safety and efficiency is converted to a two-objective problem, that is to suppress MPV fluctuations and improve ROP with vertical well constraints. Nondominated sorting genetic algorithm II is invoked to solve this problem, and the appropriate solution is selected by two references points. Moreover, a short-time scale and long-time scale optimization strategy is introduced to cope with different adjustment interval of operating variables. Finally, the simulation results based on actual drilling data, application results in a semi-physical experiment system and comparison results from a vertical well verify the effectiveness and practicability for the developed method.

关键词： Support vector regression hybrid bat algorithm Drilling optimization strategy Vertical well constraints

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Research on an identification model for mine water inrush sources based on the HBA-CatBoost algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Xu, Jin Zheng, Lulin Lan, Hong Zuo, Yujun Li, Bo Tian, Shiyu Tian, Youwen Guizhou Univ Coll Min Guiyang 550025 Peoples R China Guizhou Lindong Coal Ind Dev Co Ltd Longfeng Coal Mine Jinsha 551800 Guizhou Peoples R China Guizhou Univ Coll Resources & Environm Engn Guiyang 550025 Peoples R China

Accurate and efficient identification of water inrush sources, as one of the three critical elements of mine water hazards, is crucial for mine water management. To identify the sources of mine water inrush effectively, a model named HBA-CatBoost is introduced. This model is established on the hybrid bat algorithm (HBA)-optimized category feature gradient boosting tree (CatBoost), and the shapley additive explanation (SHAP) method is employed to elucidate the model's decision-making process. Given the prevalent occurrence of water hazards in coal seam roofs and floors in the northern Guizhou coalfield, coupled with the challenges in pinpointing water inrush sources in mines, the HBA-CatBoost model is tested at the Longfeng Coal Mine in northern Guizhou to validate its practicality. Comparative analysis with the HBA-RF, HBA-XGBoost, CatBoost, RF, and XGBoost models demonstrates that the hybrid bat algorithm significantly enhances the classification performance of the CatBoost model, resulting in improved convergence speed and classification accuracy. The HBA-CatBoost model outperforms the aforementioned models in terms of classification effectiveness, achieving accuracy, recall, precision, and F1 scores of 96.43%, 97.22%, 96.43%, and 96.61%, respectively. The SHAP method elucidates the decision mechanism of the optimal HBA-CatBoost model, highlighting the significance of sample features and bolstering the model's credibility. These outcomes underscore the superior performance of the HBA-CatBoost model and its potential for effectively identifying water inrush sources in mines.

关键词： Mine water inrush Water source identification hybrid bat algorithm CatBoost model Machine learning

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Prediction of drilling rate of penetration (ROP) using hybrid support vector regression: A case study on the Shennongjia area, Central China

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 181卷 106200-000页

作者： Gan, Chao Cao, Wei-Hua Wu, Min Chen, Xin Hu, Yu-Le Liu, Kang-Zhi Wang, Fa-Wen Zhang, Suo-Bang China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China Chiba Univ Dept Elect & Elect Engn Chiba 2638522 Japan China Univ Geosci Fac Engn Wuhan 430074 Hubei Peoples R China Chinese Acad Geol Sci China Deep Explorat Ctr SinoProbe Ctr Beijing 100037 Peoples R China Hubei Inst Urban Geol Engn Wuhan 430000 Hubei Peoples R China Seventh Geol Team Hubei Geol Bur Yichang 443100 Peoples R China

Rate of penetration (ROP) prediction is crucial for the optimization and control in drilling process due to its vital role in maximizing the drilling efficiency. This paper proposes a novel intelligent model to predict the drilling ROP considering the process characteristics. First, the geological background and the drilling process of the case study are described. Based on the mechanism and frequency spectrum analysis, the strong nonlinearity and different low-frequency and high-frequency data noises between the data variables are detected. After that, the intelligent model is established via three stages. In the first stage, a wavelet filtering method is introduced to reduce these noises in the drilling data. In the next stage, the model inputs are determined by the mutual information method, which significantly decreased the model redundancy. In the last stage, a hybrid bat algorithm is proposed to optimize the hyper-parameters of the support vector regression model. Finally, the proposed model is validated by using the data from a drilling site in the Shennongjia area, Central China. The results demonstrate that the proposed method outperforms eight well-known methods and another three methods without different data preprocessing procedures in prediction accuracy.

关键词： Rate of penetration Support vector regression hybrid bat algorithm Prediction model Drilling process

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bat and hybrid bat Meta-Heuristic for Quality of Service-Based Web Service Selection

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JOURNAL OF INTELLIGENT SYSTEMS 2017年第1期26卷 123-137页

作者： Podili, Prashanth Pattanaik, K. K. Rana, Prashanth Singh Atal Bihari Vajpayee Indian Inst Informat Technol A-117Morena Link Rd Gwalior 474015 MP India Indian Inst Technol Sch Comp Engn Hyderabad Andhra Pradesh India Thapar Inst Engn & Technol Patiala Punjab India

Efficient QoS-based service selection from a pool of functionally substitutable web services (WS) for constructing composite WS is important for an efficient business process. Service composition based on diverse QoS requirements is a multi-objective optimization problem. Meta-heuristic techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and variants of PSO have been extensively used for solving multi-objective optimization problems. The efficiency of any such meta-heuristic techniques lies with their rate of convergence and execution time. This article evaluates the efficiency of bat and hybrid bat algorithms against the existing GA and Discrete PSO techniques in the context of service selection problems. The proposed algorithms are tested on the QWS data set to select the best fit services in terms of maximum aggregated end-to-end QoS parameters. hybrid bat is found to be efficient for service composition.

关键词： Web service composition meta-heuristic particle swarm optimization (PSO) bat algorithm hybrid bat algorithm

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