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Selection of Optimal Well Trajectory Using multi-objective genetic algorithm and TOPSIS Method

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第12期48卷 16831-16855页

作者： Yavari, Hossein Qajar, Jafar Aadnoy, Bernt Sigve Khosravanian, Rasool Amirkabir Univ Technol Dept Petr Engn Tehran Iran Univ Utrecht Dept Earth Sci Utrecht Netherlands Shiraz Univ Sch Chem & Petr Engn Dept Petr Engn Shiraz Iran Univ Stavanger Dept Energy & Petr Engn Stavanger Norway

This study presents a novel approach for optimizing well paths in extended reach drilling (ERD) wells. Different trajectories can be used for ERD wells, each with its pros and cons. Previous research overlooked certain objective functions in single-objective optimization and lacked an autonomous method for selecting the best solution from Pareto optimal solutions in multi-objective optimizations. Furthermore, they lacked comparing different profiles in well design. Risk assessment and operational factors, which greatly influence optimization and drilling success, were insufficiently considered. This study utilized the multi-objective genetic algorithm (MOGA) and the technique for order preference by similarity to an ideal solution (TOPSIS) method to select the optimal well path based on torque, wellbore length, risk (e.g., keyseat), and required tools. First, all possible trajectories were determined, and MOGA identified the optimal path with minimal torque and length. The fuzzy decision-making method automatically selected the best solution from the Pareto optimal solution set. The associated risks and required tools are evaluated for each trajectory. Finally, the TOPSIS method selected the optimal trajectory based on torque, length, risks, and required tools. The case study demonstrated that the undersection path was the most advantageous trajectory for ERD wells, with a 60% closeness to the ideal state. The multiple build trajectory achieved 57% closeness, while the build and hold and double build paths had lower closeness values (43 and 28%, respectively). Consequently, it can be inferred that in the context of ERD wells, it is preferable to carry out the deviation process at deeper depths.

关键词： multi-objective genetic algorithm Optimization Torque and drag Extended reach drilling TOPSIS

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Artificial neural network-based intrusion detection system using multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTER SECURITY 2023年第3-4期21卷 320-335页

作者： Patel, N. D. Mehtre, B. M. Wankar, Rajeev Inst Dev & Res Banking Technol IDRBT Ctr Excellence Cyber Secur Hyderabad India Univ Hyderabad UoH Sch Comp & Informat Sci SCIS Hyderabad India

With recent advances in cyber-attacks, traditional rule-based intrusion detection systems are not adequate to meet the present-day challenge. Recently machine learning-based intrusion detection system (IDS) has been proposed to detect such advanced/unknown cyber-attacks. The performance of such machine learning-based IDS largely depends upon the feature set used. Generally, using more features increases the accuracy of attack detection and increases detection time. This paper proposes a new network intrusion detection system based on an artificial neural network (ANN), which uses a multi-objective genetic algorithm to satisfy the requirements: accuracy of attack detection and faster response. The performance of the proposed method is tested by using the KDD'99, NSL-KDD, and CIC-IDS-2017 datasets. The results show that the performance of the proposed method is better than the existing methods. Besides, the new process provides a trade-off on the number of features used vs. accuracy and time for detection.

关键词： intrusion detection system IDS advanced persistent threat KDD'99 NSL-KDD CIC-IDS-2017 feature selection artificial neural network ANN multi-objective genetic algorithm

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Improvement in textured hole-entry hybrid journal bearing system by using multi-objective genetic algorithm

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2022年第1期44卷 1-19页

作者： Yadav, Saurabh K. Thakre, Gananath D. Khatri, Chandra B. Inst Infrastruct Technol Res & Management IITRAM Dept Mech & Aerosp Engn Ahmadabad Gujarat India Indian Inst Petr Dehra Dun 248001 Uttarakhand India Shri Ramswaroop Mem Coll Engn & Management Dept Mech Engn Lucknow Uttar Pradesh India

In the past few decades, various studies have been performed by using textured surfaces in bearings to improve static performance characteristics friction and improve load-carrying capacity of the bearing. Surface texture also results in changes in dynamic performance characteristics of the journal bearing. The present work aims to improve the dynamic performance parameter and stability with the help of optimized textured design. As the calculation of bearing stiffness and damping parameter is quite a complex procedure, therefore, to obtain optimum texture parameter, a multi-objective genetic algorithm is used. A finite element model with the use of a genetic algorithm is developed to improve bearing performance parameters. With the use of optimum texture dimensions up to 25% improvement in the value of stability parameter is observed. It is expected that the results will be quite helpful for the bearing designer to make a stable and efficient bearing.

关键词： Surface texture Hybrid Journal Bearing multi-objective genetic algorithm Power-law lubricant

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multi-objective optimization of μ-EDM parameters for μ-hole drilling on Hastelloy C 276 super alloy using Response Surface Methodology and multi-objective genetic algorithm

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CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY 2022年 39卷 115-133页

作者： Dutta, Souradeep Sarma, Deba Kumar Natl Inst Technol Meghalaya Dept Mech Engn Shillong 793003 India Natl Inst Technol Meghalaya Dept Mech Engn Shillong 793003 Meghalaya India

Hastelloy C 276 is one of the nickel-based super alloys which has an excellent resistance against corrosion at high temperature. It has a major application in nuclear and aerospace industry. Micro-electrical discharge machining (mu-EDM) is a highly accurate and precise non-conventional micro-machining process to fabricate this alloy. Thus, in this research paper an effort has been made to examine the impact of input process parameters like capacitance, gap voltage and pulse on time (t(on)) on the response variables i.e., material removal rate (MRR), tool wear rate (TWR) and diametral overcut (DOC) during mu-EDM of Hastelloy C 276. Response Surface Methodology (RSM) is a collection of statistical and mathematical tools which determine the relationship between input and output characteristics. Box-Behnken design (BBD) is a special type of RSM based design that reduces the no of experiments without affecting the variation between the input and response parameters. RSM based BBD is used in the current study to develop the mathematical models for each response variable. RSM based desirability function (RSM-DF) which convert multiple responses into a scale free desirability, is used as an optimization tool in the present study. The current work used the multi -objective genetic algorithm (MOGA), a non-conventional searching based multi-response optimization al-gorithm, to optimize the machining parameters for maximum MRR along with minimum TWR and DOC. The lowest machining time is achieved by experiment no 10 and the least entry mu-hole diameter is drilled by experiment no 15. ANOVA analysis accomplished that capacitance has the highest significant contribution towards MRR, TWR and DOC. 3D response surface plot discloses that MRR, TWR and DOC is increased with capacitance until 5600 pF, 5600 pF and 8900 pF and then there is little reduction took place for all the three responses. The optimization result from RSM-DF is capacitance of 1001.560 pF, gap voltage of 137.6670 vo

关键词： Micro-electrical discharge machining Material removal rate Tool wear rate Diametral overcut Response Surface Methodology multi-objective genetic algorithm

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A novel multi-objective genetic algorithm approach to address class imbalance for disease diagnosis

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International Journal of Information Technology (Singapore) 2023年第2期15卷 1151-1166页

作者： Jain, Anju Ratnoo, Saroj Kumar, Dinesh CSE Department Guru Jambheshwar University of Science and Technology Haryana Hisar India Department of ECE Delhi Technological University (DTU) Shahbad Daulatpur Main Bawana Road New Delhi 42 India

The adverse consequences of class imbalance problem are prevalent while performing classification for disease diagnosis. Such classifiers predict most of the examples of negative class (instances with non-diseased label) correctly but fail to make correct predictions for the positive class examples (instances with diseased label). Since misclassification costs can be very high for a sensitive field like disease diagnosis, addressing the class imbalance issue becomes of utmost important. A number of sampling techniques have been applied for balancing data. However, these techniques reduce the overall accuracy of classifier models. This is due to the fact that there is an issue of trade-off between sensitivity and specificity of such classifiers. This paper first proposes a GA-based undersampling technique with a weighted fitness function to determine the trade-off between sensitivity and specificity followed by a multi-objective genetic algorithm (MOGA) approach to address the class imbalance problem for disease diagnosis. To determine the trade-off between sensitivity and specificity manually is an arduous task. The MOGA approach takes the two extreme training samples from Pareto optimal solutions, one optimally tuned with respect to sensitivity and the other one optimally tuned with respect to specificity on validation data. Two decision tree classification models are built based on these two training sets. The models are named as sensitivity prioritized model (SEPM) and specificity prioritized model (SPPM) respectively. These models are combined to make predictions on the test data. The results obtained through extensive experimentation confirm that the proposed multi-objective scheme makes correct predictions on minority class (SE) without compromising the correct prediction rate on the majority class (SP). © 2020, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Class imbalance problem Classification for medical datasets related to disease diagnosis genetic algorithm multi-objective genetic algorithm

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Turning of Carbon Fiber Reinforced Polymer (CFRP) Composites: Process Modeling and Optimization using Taguchi Analysis and multi-objective genetic algorithm

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MANUFACTURING LETTERS 2022年 33卷 29-40页

作者： Rob, S. M. Abdur Srivastava, Anil K. Univ Texas Rio Grande Valley 1201 W Univ Dr Edinburg TX 78539 USA

Carbon Fiber Reinforced Polymer (CFRP) composites have been widely used in aerospace, automotive, nuclear, and biomedical industries due to their high strength-to-weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties in non-oxidative atmospheres. Machining of CFRP composites has always been a challenge for manufacturers. In this research, a comparative study was performed between the optimal machining parameters of coated and uncoated carbide inserts obtained from the multi-objective genetic algorithm during turning of CFRP composites. It was found that coated carbide inserts provide lower tool wear and surface roughness, but higher cutting forces compared to those of uncoated carbide inserts during turning of CFRP composites. Taguchi Analysis was performed to investigate the effects of machining parameters (cutting speed, feed rate and depth of cut) on the output characteristics including cutting force, surface roughness and tool wear. The feed rate was found as the most significant machining parameter in turning of CFRP composites to minimize cutting force and surface roughness using both coated and uncoated carbide inserts. However, feed rate and cutting speed has been found as the most significant machining parameters for coated and uncoated carbide inserts respectively to minimize the tool wear. Regression Analysis has been performed to develop mathematical models for cutting force, surface roughness and tool wear as a function of cutting speed, feed rate and depth of cut. Higher R2 values and well fitted regression lines of normal probability plots in regression analysis indicate that the coefficients of mathematical models are statistically significant. The significance of this study is to emphasize the differences of performances between coated and uncoated carbide inserts during turning of CFRP composites in terms of cutting force, tool wear and surface roughness with the combination of different machining parameters (cu

关键词： CFRP composites Taguchi Analysis multi-objective genetic algorithm

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Trading Strategy Optimisation with a multi-objective genetic algorithm 11

Trading Strategy Optimisation with a Multi-objective Genetic...

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11th International Conference on Information Technology - IoT and Smart City (ITIoTSC)

作者： Liu, Yu Univ Western Australia Dept Comp Sci & Software Engn Perth WA 6009 Australia

ISBN: (纸本)9798350328356;9798350328349

algorithmic trading, a significant area of research in artificial intelligence, faces the challenges of interpreting and processing extensive and dynamic data. This environment is ideal for implementing adaptive algorithms, such as the evolutionary algorithm NSGA-II used in this paper, notable for its effectiveness in multi-objective optimisation problems. We apply this algorithm, in conjunction with technical indicators, to explore and exploit the search space provided by BTC/AUD market data for a specific trading strategy. Our objective is to demonstrate how NSGA-II, with an optimal parameter configuration, can balance net profit and risk to optimise trading outcomes, as evidenced in the Pareto-front results. Our findings show that by optimizing the parameter values, our NSGA-II driven strategy significantly outperformed a buy-and-hold strategy over the same period.

关键词： BTC/AUD Trading multi-objective genetic algorithm Optimization Risk and Return Evaluation

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Optimization of intermittent oil recovery mechanism based on multi-objective genetic algorithm 35

Optimization of intermittent oil recovery mechanism based on...

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35th Chinese Control and Decision Conference (CCDC)

作者： Wu, Jingsong Jiang, Mingzheng Dong, Kangxing Yang, Hukun Northeast Petroleum Univ Sch Mech Sci & Engn Daqing Peoples R China

ISBN: (纸本)9798350334722

With the continuous exploitation of oil fields, the problem of long-term inefficient operation of some pumping units is common in major oil fields in China. The intermittent oil recovery mechanism can effectively avoid the wear and tear of empty pumping while reducing electrical energy consumption. The Pareto multi-objective genetic algorithm is used to optimize the optimal downtime of the pumping units from the perspective of energy saving in the oil recovery system, to maximize the efficiency of oil recovery while minimizing power consumption. A comparison of the experimental results showed that the intermittent oil recovery mechanism was optimized to save 21.45% of energy consumption and improve the system efficiency by 38.85%. This method solves the problems of empty pumping and inefficiency of pumping units and achieves the purpose of reducing the mechanical wear and tear of oil recovery machines, saving electrical energy, and improving the overall development benefit of the oil field.

关键词： pumping units intermittent pumping energy saving multi-objective genetic algorithm oil extraction efficiency

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Optimal answer generation by equivalent transformation incorporating multi-objective genetic algorithm

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SOFT COMPUTING 2022年第19期26卷 10535-10546页

作者： Miura, Katsunori Powell, Courtney Munetomo, Masaharu Otaru Univ Dept Informat & Management Sci 3-5-21 Midori Otaru Hokkaido 0478501 Japan Hokkaido Univ Informat Initiat Ctr Kita Ku Kita 11Nishi 5 Sapporo Hokkaido 0600811 Japan

This paper proposes a framework for time-efficiently finding multiple answers that meet multiple logical constraints and are Pareto-optimal for multiple objective functions. The proposed framework determines a set of optimal answers by iteratively replacing a set of definite clauses. Clause replacement is performed using a SAT solver consisting of equivalent transformation rules (ETRs). An ETR replaces a definite clause with one or more clauses while preserving the declarative meaning of the union of the original clause and problem clauses. To efficiently find optimal answers, in this paper, we define a new class of ETRs that are generated based on the evaluation results of a multi-objective genetic algorithm (MOGA) and propose a method for generating ETRs that belong to the new class. ETRs belonging to the new class help to replace definite clauses according to user objectives such as cost-benefit performance, reliability, and financial constraints. Thus, a SAT solver that uses the new ETR class in addition to extant ETR classes can preferentially replace definite clauses that produce the optimal answer for user objectives. Experimental results indicate that the proposed framework can significantly reduce the computation time and memory usage necessary to determine a set of optimal answers for user objectives.

关键词： Equivalent transformation multi-objective genetic algorithm Clause replacement Optimization Pareto-optimal answer Predicate logic

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Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2022年第3期60卷 663-681页

作者： Deng, Xiongshi Li, Min Deng, Shaobo Wang, Lei Sch Informat Engn Nanchang Inst Technol Nanchang 330099 Jiangxi Peoples R China Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang 330099 Jiangxi Peoples R China

Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked using an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes' group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 14 publicly available microarray expression datasets. The experimental results show that XGBoost-MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.

关键词： XGBoost Feature selection Microarray gene expression multi-objective genetic algorithm Classification

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