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Many-objective optimization based path planning of multiple UAVs in oilfield inspection

APPLIED INTELLIGENCE 2022年第11期52卷 12668-12683页

作者： Li, Kun Yan, Xinxin Han, Ying Ge, Fawei Jiang, Yu Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China CCTEG Shenyang Res Inst Fushun 113122 Liaoning Peoples R China

Unmanned aerial vehicles (UAVs) inspection can be considered as a path planning problem for the UAVs, which needs UAVs to traverse all task points one by one by avoiding some obstacles. In most current researches, only single objective function is used in the path planning for the UAVs, and some other objectives are seldom considered together, like: the number of UAVs, the required inspection time and so on. Therefore, in order to overcome the defect of using single objective function to plan the path of UAVs, a many-objective optimization based multiple UAVs path planning method is studied in this paper. Firstly, four objective functions are chosen which include the flight distance, the flight stability, the number of UAVs and the time offset of reaching the task point;considering the arrival time of UAVs at each task point is an interval value in the practical application, the time offset of reaching the task point is set as an interval objective function, which is handled by the matter-element extension method in this paper. Then, an improved nsga-iii algorithm is proposed to solve the established many-objective optimization problem, which uses fruit fly optimization algorithm (FOA) to replace the genetic algorithm (GA) in the nsga-iii algorithm. Finally, several evaluation indictors (Spread, generational distance (GD), inverse generational distance (IGD), and running time) are used to choose the optimal flight paths of multiple UAVs. Through simulation comparisons with other algorithms, it is concluded that the improved nsga-iii algorithm is more effective in the path planning of UAVs.

关键词： Oilfield inspection Many-objective optimization nsga-iii algorithm Path planning Fruit fly optimization algorithm

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New hybrid nsga-iii&SPEA/R to multi-object optimization in a half-car dynamic model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2020年第6期234卷 1660-1671页

作者： Dao, Dinh-Nam Guo, Li-Xin Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China Le Quy Don Tech Univ Control Technol Coll Hanoi Vietnam

In this article, we conducted a new hybrid method between Non-dominated Sorting Genetic algorithm II (nsga-iii) and SPEA/R (Hnsga-iii&SPEA/R). This method is implemented to find the optimal values of the powertrain mount system stiffness parameters. This is the task of finding multi-objective optimization involving six simultaneous optimization goals: mean square acceleration and mean square displacement of the powertrain mount system. A hybrid Hnsga-iii&SPEA/R has proposed with the integration of Strength Pareto evolutionary algorithm-based reference direction for Multi-objective (SPEA/R) and Many-objective optimization genetic algorithm (nsga-iii). Several benchmark functions are tested, and results reveal that the Hnsga-iii&SPEA/R is more efficient than the typical SPEA/R and nsga-iii. Powertrain mount system stiffness parameters optimization with Hnsga-iii&SPEA/R is simulated. It proved the potential of the Hnsga-iii&SPEA/R for powertrain mount system stiffness parameter optimization problem.

关键词： nsga-iii algorithm SPEA R algorithm optimal solution powertrain mount system stiffness multi-object optimization engine mount

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An Improved Multi-Objective Trajectory Planning algorithm for Kiwifruit Harvesting Manipulator

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IEEE ACCESS 2023年 11卷 65689-65699页

作者： Li, Xiao Lv, Hailin Zeng, Detian Zhang, Qi Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Key Lab Intelligence Integrated Automat Guangxi Un Guilin 541004 Peoples R China

Trajectory planning is always a hot issue for harvesting manipulators, considering the limitations of the manipulator mechanical structure and other nonlinear factors that result in long time, big jerk and high energy consumption of the manipulator in tracking the picking path. The cubic spline algorithm, fifth-order polynomial interpolation algorithm, a fusing cubic spline and fifth-order polynomial algorithm aim to shorten harvesting time, reduce jerk and enhance the robustness of the harvesting manipulator, respectively. Then the trajectory planning problem is converted into a multi-objective optimization solution problem. A non-dominated sorting genetic algorithm-the third version (nsga-iii) technology is used to address multi-objective optimization constrained problems with time, energy consumption and jerk optimization objectives. The Pareto optimal set containing the multiple constraints is obtained through the nsga-iii algorithm. Compared to a non-dominated sorting genetic algorithm-the second version (nsga-II) technique, optimal solution with a shorter time is chosen from the non-dominated Pareto optimal set and sent to the manipulator controller actuator. Under the robot operating system (ROS), the cubic spline algorithm, fifth-order polynomial interpolation algorithm, a fusing cubic spline and fifth-order polynomial algorithm with the structural coefficient obtained respectively through nsga-iii algorithm and nsga-II algorithm are simulated. The simulation results show that the nsga-iii algorithm has a better result in addressing the trajectory planning problem. The experimental analysis of the harvesting manipulator verifies the proposed scheme that the cubic spline algorithm based on the nsga-iii algorithm enables the harvesting manipulator to track the picking path faster and smoother, which is effective and feasible for reducing the time and improving the harvesting efficiency of kiwifruit.

关键词： Trajectory planning multi-objective optimization cubic spline algorithm nsga-iii algorithm

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A Motif-based Mission Planning Method for UAV Swarms Considering Dynamic Reconfiguration

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DEFENCE SCIENCE JOURNAL 2018年第2期68卷 159-166页

作者： Liu, Jiajie Wang, Weiping Li, Xiaobo Wang, Tao Wang, Tongqing Natl Univ Def Technol Coll Syst Engn Changsha Hunan Peoples R China

Influenced by complex terrain conditions of combat environments and constrained by the level of communication technology, communication among unmanned aerial vehicles (UAV) is greatly restricted. In light of this situation, mission planning for UAV swarms under limited communication has become a difficult problem. This paper introduces motifs as the basic unit of configuration and proposes a motif-based mission planning method considering dynamic reconfiguration. This method uses multidimensional dynamic list scheduling algorithm to generate a mission planning scheme based on the motif-based swarm configuration solution. Then it incorporates order preserved operators with nsga-iii algorithm to find Pareto front solutions of all possible mission planning schemes. The feasibility of this mission planning method is validated through a case study.

关键词： Mission planning UAV swarms Dynamic reconfiguration MDLS nsga-iii algorithm

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Multi-objective control chart design optimization using nsga-iii and MOPSO enhanced with DEA and TOPSIS

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 50卷 17-39页

作者： Tavana, Madjid Li, Zhaojun Mobin, Mohammadsadegh Komaki, Mohammad Teymourian, Ehsan La Salle Univ Distinguished Chair Business Analyt Business Syst & Analyt Dept Philadelphia PA 19141 USA Univ Paderborn Business Informat Syst Dept Fac Business Adm & Econ Warburger Str 100 D-33098 Paderborn Germany Western New England Univ Ind Engn & Engn Management Dept Springfield MA 01119 USA Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA Oregon State Univ Sch Mech Ind & Mfg Engn Corvallis OR 97331 USA

X-bar control charts are widely used to monitor and control business and manufacturing processes. This study considers an X-bar control chart design problem with multiple and often conflicting objectives, including the expected time the process remains in statistical control status, the type-I error, and the detection power. An integrated multi-objective algorithm is proposed for optimizing economical control chart design. We applied multi-objective optimization methods founded on the reference-points-based non-dominated sorting genetic algorithm-II (nsga-iii) and a multi-objective particle swarm optimization (MOPSO) algorithm to efficiently solve the optimization problem. Then, two different multiple criteria decision making (MCDM) methods, including data envelopment analysis (DEA) and the technique for order of preference by similarity to ideal solution (TOPSIS), are used to reduce the number of Pareto optimal solutions to a manageable size. Four DEA methods compare the optimal solutions based on relative efficiency, and then the TOPSIS method ranks the efficient optimal solutions. Several metrics are used to compare the performance of the nsga-iii and MOPSO algorithms. In addition, the DEA and TOPSIS methods are used to compare the performance of nsga-iii and MOPSO. A well-known case study is formulated and solved to demonstrate the applicability and exhibit the efficacy of the proposed optimization algorithm. In addition, several numerical examples are developed to compare the nsga-iii and MOPSO algorithms. Results show that nsga-iii performs better in generating efficient optimal solutions. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Economical control chart design nsga-iii algorithm MOPSO algorithm Data envelopment analysis TOPSIS

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Inspection and maintenance optimization for heterogeneity units in redundant structure with Non-dominated Sorting Genetic algorithm iii

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ISA TRANSACTIONS 2023年 135卷 299-308页

作者： Zhang, Aibo Hao, Songhua Xie, Min Liu, Yiliu Yu, Haoshui City Univ Hong Kong Dept Adv Design & Syst Engn Hong Kong Peoples R China Hong Kong Sci Pk Ctr Intelligent Multidimens Data Anal Hong Kong Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu Peoples R China Norwegian Univ Sci & Technol Dept Mech & Ind Engn Trondheim Norway Aalborg Univ Dept Chem & Biosci Esbjerg Denmark Aalborg Univ Dept Chem & Biosci Niels Bohrs Vei 8A DK-6700 Esbjerg Denmark

Redundant structure has been widely deployed to improve system reliability, as when one unit fails, the system can continue to function by using another one. Most existing studies rely on the similar assumption that the heterogeneous units are subject to periodic inspections and identical in terms of their aging situations and the numbers of resisted shocks. In practice, it is often adequate to trigger a unit individually in the event of a single shock, which intensifies the degradation of that unit, accordingly, requiring a sooner inspection to ensure its safety. In this study, the stochastic dependency among units is addressed firstly by introducing a novel activation sequence. Secondly, an adaptive system-level inspection policy is proposed by prioritizing the unit with a worse state. Finally, we take advantage of Monte Carlo methods to simulate the whole process and estimate two objectives, referring to the average system unavailability and maintenance cost, in a designed service time. It is found that the two objectives are contradictory through numerical examples. The Non-dominated Sorting Genetic algorithm iii (nsga-iii) algorithm, therefore, has been employed to find the optimal solutions in system unavailability and cost, which provide clues for practitioners in decision-making.(c) 2022 The Author(s). Published by Elsevier Ltd on behalf of ISA. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Activation sequence Adaptive inspection Redundant structure 1oo2 configuration Maintenance optimization nsga-iii algorithm

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Risk-limiting dispatching strategy considering demand response in multi-energy microgrids

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APPLIED ENERGY 2024年 353卷

作者： Nie, Yonghui Qiu, Yu Yang, Annan Zhao, Yan Northeast Elect Power Univ Acad Adm Off Jilin 132012 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Peoples R China

Multi-energy microgrids deploy distributed power sources with flexible generation characteristics to meet the changing needs of users. Because they comprise renewable as well as traditional energy sources, they can play an important role in the transition to clean and efficient low-carbon power generation. However, both the complexity of coordinating different power sources and random fluctuations of renewable-energy generation capacity pose significant challenges. To solve these problems, a risk-limiting dispatching strategy for multienergy microgrids is proposed that considers net load-demand response. First, a bi-directional demandresponse mechanism is introduced, and a flexible load is used as a dispatchable resource to optimize the net load curve of the system. Second, entropy-based reliability modeling is conducted to address the multiple uncertainties in system operation. Third, a risk-limiting dispatching model of the multi-energy microgrid is established that considers three optimization objectives: economy, environmental protection, and reliability. Finally, the nsga-iii algorithm is used to obtain the set of optimal solutions for the model, and a compromise solution that meets the system operation requirements is selected based on the technique for order preference by similarity to an ideal solution (TOPSIS) with a criteria importance though intercriteria correlation (CRITIC) and analytic hierarchy process (AHP, CRITIC-AHP) hybrid assignment. The simulation results show that the proposed optimal scheduling strategy improves the operational reliability index while ensuring system economy and environmental protection. This study provides a new approach for controlling system risk and ensuring continuous energy-supply capability under uncertain conditions.

关键词： Hybrid weighting Information entropy Multi-energy microgrid Net load demand response nsga-iii algorithm Operational reliability

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Many-objective optimisation tool for the design of district metered areas in pumped water distribution networks

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WATER SUPPLY 2023年第9期23卷 3789-3804页

作者： Sharma, Aniket Nandkumar Dongre, Shilpa R. Gupta, Rajesh Visvesvaraya Natl Inst Technol Dept Civil Engn Nagpur 440010 Maharashtra India

The establishment of district metered areas (DMAs) is a highly effective method to mitigate operational management difficulties and enhance the efficiency of water distribution networks (WDNs). There are several objectives associated with the DMA's design that are contingent upon the network parameters that are affected by its formation. Two previous studies considered DMA design as a three-objective problem (operational cost, average pressure and water age) and a four-objective problem (design cost, pressure deviation, resilience index (RI) and demand shortfall) for pumped and gravity networks, respectively. The problems were addressed through the implementation of a multi-phase DMA design methodology using the nsga-II and nsga-iii optimisation tools, respectively. The present work builds upon previous research by simultaneously considering five objectives in DMA design (i.e. design cost, operational cost, RI, average pressure and water age) using the nsgaiii optimisation tool in pumped water networks. In this extended approach, the pump's role in meeting nodal demands eliminates the necessity of including demand shortfall as one of the objectives. The proposed methodology has been evaluated on two benchmark networks, demonstrating its capability to identify DMA alternatives and provide solutions based on user preferences. Finally, the obtained results are compared with the previous study's findings.

关键词： district metered areas fast Newman algorithm many-objective optimisation multiple attribute decision-making nsga-iii algorithm

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Performance prediction and parameter optimization of alumina-titanium carbide ceramic micro-EDM hole machining process based on XGBoost

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS 2024年第2期238卷 310-319页

作者： Chen, Yujin Wu, Yaoguang Cheng, Mengmeng Zhu, Jihong Meng, Yanmei Mu, Xiele Guangxi Univ Coll Mech Engn Nanning Peoples R China Guangxi Univ Coll Comp & Elect Informat Nanning Peoples R China Tsinghua Univ Dept Precis Instrument Beijing Peoples R China Guangxi Univ Coll Comp & Elect Informat Nanning 530000 Peoples R China

Aiming at the difficulties in setting process parameters and the low accuracy of process performance prediction in electrical discharge machining (EDM) hole machining of alumina ceramics, a novel EDM process performance prediction method based on eXtreme Gradient Boosting (XGBoost) is proposed in this study. The independent variables selected include processing polarity (P), interelectrode voltage (V), peak current (I), pulse frequency (F), pulse width (T), and tool electrode and workpiece electrode gap (W), while the dependent variables are tool electrode length loss (ELW), tool electrode volume loss (EVW), and material removal rate (MRR). An L-18 (2(1) x 3(5)) orthogonal test is designed to obtain training samples for model development. The accuracy of the prediction results for ELW, EVW, and MRR by the XGBoost model is found to be 1.7%, 2.5%, and 9.4%, respectively. These results show a significant improvement compared to the linear regression model, with an improvement of 41.7%, 62.5%, and 19.4%, respectively. Furthermore, when compared to the support vector regression (SVR) model, the XGBoost model also shows improvement of 23.6%, 5.9%, and 12.0% for ELW, EVW, and MRR prediction accuracy, respectively. These findings suggest that the proposed XGBoost-based method is effective and accurate in predicting the performance of alumina ceramic EDM processes. In addition, the third generation nondominated sorting genetic algorithm (nsga-iii) is utilized to optimize the process parameters with both single objective and multi-objective approaches. The optimal parameters for ELW, EVW, and MRR, including the selection of machining polarity, interelectrode voltage, peak current, pulse frequency, pulse width, and tool electrode and workpiece electrode gap are obtained through the Pareto frontier of the three comprehensive optima. The average relative errors between the experimental results and the optimized results are found to be 6.46%, 10.45%, and 9.58% for ELW, EVW, and M

关键词： Micro-EDM hole machining orthogonal test XGBoost nsga-iii algorithm multi-objective optimization

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Intelligent device recognition of internet of things based on machine learning

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2024年 22卷

作者： Huang, Sheng Shanghai Inst Tourism Equipment Dept Shanghai 201418 Peoples R China

With the rapid popularization and application of Internet of Things technology, smart devices have become an indispensable part of people's daily lives. Therefore, it is crucial to accurately identify these devices as their numbers continue to grow. The research aimed to introduce a lightweight method for identifying Internet of Things devices based on network flow characteristics and scheduling algorithms. This can improve device identification accuracy while maintaining high efficiency. The research constructed a comprehensive optimization algorithm selection framework to achieve performance optimization in different scenarios. This framework took into account many factors, including network traffic characteristics, device identification requirements, and system efficiency, to ensure flexible adaptation in different scenarios and optimize overall performance. Research results showed that the proposed system had an accuracy of 96.8 % at 1-minute intervals, which increased to 99.7 % at 10-minute intervals, and reached 99.9 % at both 30-minute and 60-minute intervals. In 100 experiments, the research method improved the accuracy by an average of 1.5 % compared with the baseline. In fingerprint recognition, the overall accuracy of the long short-term memory network exceeded 90 %, with an area under the curve of 0.99. Most devices had an accuracy of over 95 % in recognition, and the recall rate remained around 90 %, the effectiveness of the method proposed in the study was further verified. The method proposed in the study not only improved the accuracy and efficiency of device recognition, but also provided powerful solutions for the field of network security. This provides useful guidance for research and practical applications in related fields.

关键词： Internet of Things technology nsga-iii algorithm LSTM neural network Communication behavior monitoring Fingerprint identification

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