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第35届中国控制与决策会议

作者： Jingsong Wu Mingzheng Jiang Kangxing Dong Hukun Yang School of Mechanical Science and Engineering Northeast Petroleum University

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 *** intermittent oil recovery mechanism can effectively avoid the wear and tear of empty pumping while reducing electrical energy *** 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 by38.85%.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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Controlled elitist multi-objective genetic algorithm joined with neural network to study the effects of nano-clay percentage on cell size and polymer foams density of PVC/clay nanocomposites

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2020年第4期139卷 2801-2810页

作者： He, Wei Bagherzadeh, Seyed Amin Shahrajabian, Hamzeh Karimipour, Arash Jadidi, Hamid Quang-Vu Bach Minjiang Univ Fujian Univ Marine Intelligent Ship Equipment Engn Res Ctr Fuzhou 350108 Fujian Peoples R China Islamic Azad Univ Najafabad Branch Dept Mech Engn Najafabad Iran Ton Duc Thang Univ Fac Environm & Labour Safety Sustainable Management Nat Resources & Environm R Ho Chi Minh City Vietnam

The parameters of foaming and nano-clay percentage on the density of polymer foam and cell size with the PVC field is studied. Cell size and density have a significant impact on the strength of foam and its insulation (including sounds and thermal insulation). By optimizing cell size and density, foam can be produced with the best mechanical properties. In foaming process of the nanocomposite samples by mass method, the design variables (input parameters) are foaming time and temperature and MMT content. The controlled elitist multi-objective GA is applied to minimize both the foam density and the cell size. To that end, the population size and the Pareto fraction are selected as 100 and 0.5, respectively. The noninferior solution obtained by the controlled elitist multi-objective GA is illustrated. When both the MMT and the temperature are high, the resulting foam does not have ideal characteristics.

关键词： Statistical neural network multi-objective genetic algorithm Polymer foam density Clay nanocomposites

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A multi-objective genetic algorithm for a special type of 2D orthogonal packing problems

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APPLIED MATHEMATICAL MODELLING 2020年第Part1期77卷 66-81页

作者： Erozan, Ihsan Caliskan, Enes Kutahya Dumlupinar Univ Kutahya Turkey

In this study, a multi-objective model is proposed for a specific two-dimensional orthogonal packing problem. The model has three objectives, namely: (i) to minimise the distances between the centres of rectangular items, (ii) to use a minimisation function to place the related rectangular items close to each other, and (iii) to minimise the area of each rectangular item that falls outside of the radius. In addition to the weighted-sum method, the model uses the conic scalarisation method for scalarisation of the nonlinear multi-objective optimisation problem. The proposed model is a hybridisation of a placement procedure and a multi-objective genetic algorithm, and introduces the concept of the defensive radius to the literature. The model is tested on a randomly generated set of instances and some test problems. The computational results validate the quality of the solutions and the effectiveness of the proposed multi-objective model compared to the cluster boundary algorithm. Besides, the results show that the conic scalarisation outperforms the weighted-sum method in terms of scalar results in most cases. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Orthogonal packing problem multi-objective genetic algorithm Defensive radius Conic scalarisation

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Workload Allocation in IoT-Fog-Cloud Architecture Using a multi-objective genetic algorithm

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JOURNAL OF GRID COMPUTING 2020年第1期18卷 43-56页

作者： Abbasi, Mahdi Pasand, Ehsan Mohammadi Khosravi, Mohammad R. Bu Ali Sina Univ Engn Fac Dept Comp Engn Hamadan Hamadan Iran Persian Gulf Univ Dept Comp Engn Bushehr Iran Shiraz Univ Technol Dept Elect & Elect Engn Telecommun Grp Shiraz Iran

With the rapid growth of Internet-of-Things (IoT) applications, data volumes have been considerably increased. The processing resources of IoT nodes cannot cope with such huge workloads. Processing parts of the workload in clouds could solve this problem, but the quality of services for end-users will be decreased. Given the latency reduction for end-users, the concept of processing in the fog devices, which are at the edge of the network has been evolved. Optimizing the energy consumption of fog devices in comparison with cloud devices is a significant challenge. On the other hand, providing the expected-quality of service in processing the requested workloads is highly dependent on the propagation delay between fog devices and clouds, which due to the nature of the distribution of clouds with the different workloads, is highly variable. To date, none of the proposed solutions has solved the problem of workload allocation given the criteria of minimizing the energy and delay of fog devices and clouds, simultaneously. This paper presents a processing model for the problem in which a trade-off between energy consumption and delay in processing workloads in fog is formulated. This multi-objective model of the problem is solved using NSGAII algorithm. The numerical results show that by using the proposed algorithm for workload allocation in a fog-cloud scenario, both of energy-consumption and delay can be improved. Also, by allocating 25% of the IoT workloads to fog devices, the energy consumption and delay are both minimized.

关键词： Fog computing Internet of Things multi-objective genetic algorithm Workload distribution Energy Delay

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Applying multi-objective genetic algorithm (MOGA) to optimize the energy inputs and greenhouse gas emissions (GHG) in wetland rice production

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ENERGY REPORTS 2020年 6卷 2988-2998页

作者： Elsoragaby, Suha Yahya, Azmi Mahadi, Muhammad Razif Nawi, Nazmi Mat Mairghany, Modather Elhassan, Sami Mustafa M. Kheiralla, A. F. Univ Putra Malaysia Fac Engn Dept Biol & Agr Engn Serdang 43400 Selangor De Malaysia Univ Khartoum Fac Engn Dept Agr & Biol Engn Khartoum Sudan Univ Teknol PETRONAS Inst Self Sustainable Bldg Seri Iskandar Perak Malaysia

Efficient use of energy in crops production will minimize greenhouse gas emission (GHG), prevent destruction of natural resources, and promote sustainable agriculture as an economical crop production system. The aim of this study is applying the multi-objective genetic algorithm MOGA to optimize the energy inputs and reduce the greenhouse gas emissions (GHG) for wetland rice production in Malaysia. The developed multi-objective genetic algorithm (MOGA) model, showed an excess of energy inputs used by the farmers more than the required energy by 37.8% and 40% for the transplanting and broadcast seeding methods. The potential of GHG emissions reduction by MOGA was computed as 95.89 and 236.13 kg CO2eq/ha. Nitrogen represents the highest contributor to the reduction of both, total energy input and total GHG emissions in the two cultivation methods transplanting and broadcast seeding methods. Despite lower consumption of inputs by MOGA, crop yield is estimated at 9.4 ton/ha in transplanting and 9.2 ton/ha in broadcast seeding, which is close to the region's maximum under current condition. The main finding that MOGA model showed an excess of energy inputs used and the potential of GHG emissions reduction was 19.6% and 46.37%. for the transplanting and broadcast seeding methods. (C) 2020 The Author(s). Published by Elsevier Ltd.

关键词： Energy analysis Greenhouse gas emissions (GHG) Optimization multi-objective genetic algorithm

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Hydraulic performance and parameter optimisation of a microporous ceramic emitter using computational fluid dynamics, artificial neural network and multi-objective genetic algorithm

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BIOSYSTEMS ENGINEERING 2020年 189卷 11-23页

作者： Zhou, Wei Zhang, Lin Wu, Pute Cai, Yaohui Zhao, Xiao Yao, Chunping Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Inst Soil & Water Conservat Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China

As the core of an irrigation system, microporous ceramic emitters (MPCEs) are closely related to the design, operation, and management of underground irrigation systems. In this study, computational fluid dynamics (CFD), an artificial neural network (ANN), and a multi-objective genetic algorithm (MOGA) were used to investigate the hydraulic performance of an MPCE and conduct parameter optimisation. A CFD software package was used to analyse the influence of the working parameters, structural parameters, and material properties on the flow characteristics of MPCE. Subsequently, an ANN and MOGA were used to optimise fabrication cost, flow rate, and flow index of the emitter. The results showed that a decrease in the bottom thickness or wall thickness of the MPCE increased the flow index of the emitter and the sensitivity of the flowrate to changes in pressure. Low working pressure was conducive to maintaining the active irrigation characteristic of the MPCE but the flowrate decreased. For crops with low water requirement, these conditions are ideal. The flow in the MPCE microchannel was mainly affected by the viscous resistance, whereas the inertial resistance only had an effect when the flow velocity was large. For field applications, the parameters can be optimised in the range obtained by the MOGA optimisation depending on the requirements of crop water demand, irrigation quality, and fabrication cost to achieve optimal irrigation performance. The results of this study provide references for the standardised fabrication of MPCEs. (C) 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Subsurface drip irrigation Microporous ceramic emitters Computational fluid dynamics Artificial neural network multi-objective genetic algorithm

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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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Optimization of Halbach magnetic roller for eddy current separation based on the response surface method and multi-objective genetic algorithm

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JOURNAL OF CLEANER PRODUCTION 2021年 278卷 123531-123531页

作者： Bin, Cao Yi, Yuan Yerbol, Arman Lei, Feng Zhou Zongqiang Wang Tiansheng Qiang, Wang Northeastern Univ Sch Met Shenyang 110819 Peoples R China Northeastern Univ Minist Educ Key Lab Electromagnet Proc Mat Shenyang 110819 Peoples R China

Performance optimization of the magnetic roller of eddy current separator is an issue of great significance in the recycling industry. In this study, the Halbach array is proposed for the first time to be used in the eddy current separator. And the new concepts of area field intensity and magnet efficiency density are proposed to quantitatively evaluate the magnetic field intensity around the magnetic roller surface and the utilization efficiency of the magnet respectively. Then the effects of various structural parameters of the Halbach magnetic roller on the area field intensity and magnet efficiency density were systematically investigated by employing finite element analysis and response surface methodology. The results showed that the regression models of the area field intensity (R-2 = 0.9846, Pred.R-2 Y = 0.9420) and magnet efficiency density (R-2 = 0.9946, Pred.R-2 = 0.9782) are extremely significant and have good prediction capability. The magnetic roller can form a stronger magnetic field by selecting a larger magnetic block, and the utilization efficiency of the magnet is higher when the magnetic block's shape tends to be flat or has larger curvature. Furthermore, a multi-objective optimization model with the area field intensity, magnet efficiency density, and magnetic pole number is established, and a multi-objective genetic algorithm is applied to get the optimal combinations of various structural parameters. It was found that the points of the Pareto-optimal solution are concentrated on a spatial surface, which reflects the trade-off relationship among the three objectives. It also shows that the performance parameters of many Pareto-optimal solutions are superior to that of previous studies and the eddy current separators found in the vicinity. The proposed method framework and the results are useful for the design and optimization of the magnetic roller, which will improve the separation efficiency of eddy current separation. (C) 2020 Elsevier Ltd.

关键词： Halbach magnetic roller Finite element analysis Response surface methodology multi-objective genetic algorithm Eddy current separator

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Optimal Route Search Based on multi-objective genetic algorithm for Maritime Navigation Vessels

Optimal Route Search Based on Multi-objective Genetic Algori...

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Human Interface and the Management of Information (HIMI) part of International Conference on Human-Computer Interaction (HCII)

作者： Saga, Ryosuke Liang, Zhipeng Hara, Naoyuki Nihei, Yasunori Osaka Prefecture Univ 1-1 Gakuen Cho Sakai Osaka 5998531 Japan

ISBN: (纸本)9783030500160;9783030500177

Ocean research requires regular collection of ocean data, wherein an autonomous robotic ship is usually used. However, in contrast to collecting land-based data, collecting sea level data face the following problems. First, robot ships are affected by sea surface winds, waves, and tides, with constantly changing strength and direction. Second, hull collisions must be prevented when multiple ships are working simultaneously. Third, given the limitation of the electric power of the autonomous sailing ship, the electric power consumption of the robot ship must be considered when collecting over a wide sea. Fourth, fixed obstacles, such as an island on the sea surface, must be avoided. Given such issues, no effective navigation route search system is currently available. In this work, a navigation route system for complex situations on the sea surface was designed on the basis of the actual situation. Clustering method was used to classify collection points according to distance based on the number of robot ships, and a multi-objective genetic algorithm was used to determine the optimal path for each classification.

关键词： Ocean research multi-objective genetic algorithm Optimal route search

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Optimal Design of Robot Wireless Transmission System Based on multi-objective genetic algorithm 5

Optimal Design of Robot Wireless Transmission System Based o...

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5th International Conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Fu Qi-ye Liu Wei Li You-hong Li Wei-yong Guangdong Aib Polytech Coll Intelligent Engn Coll Guangzhou Peoples R China Guangdong Univ Technol Huali Coll Guangzhou Peoples R China

ISBN: (数字)9781728199283

ISBN: (纸本)9781728199283

Wireless energy transmission technology is the key technology for robots to achieve lightweight, sustainable, and cable-free work. The magnetic coupling resonance wireless charging method, which must consider the impact of the working environment on the wireless energy transmission system, can be equivalent to the mutual inductance coupling circuit due to the eddy current effect of obstacles. In this paper, the transmission system was modeled by the coupled circuit theory, and the voltage gain coefficient transmission equation was derived. The characteristics of the wireless power transmission system under the influence of the eddy current effect was analysed. A coupling circuit for a wireless power transmission system was designed, and the system parameter values were obtained using MATLAB simulation. Through multi-objective genetic algorithm analysis, the parameter design of transmission system was optimized.

关键词： Wireless transmission MATLAB simulation metal obstacles multi-objective genetic algorithm

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