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Time Series Forecasting Using a Two-Level multi-objective genetic algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans

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algorithmS 2018年第8期11卷 123-123页

作者： Al-Douri, Yamur K. Hamodi, Hussan Lundberg, Jan Lulea Univ Technol Div Operat & Maintenance Engn SE-97187 Lulea Sweden Univ Mosul Coll Engn Dept Mech Engn AZ-6321 Mosul Iraq

The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

关键词： ARIMA model data forecasting multi-objective genetic algorithm regression model

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Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a multi-objective genetic algorithm

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ENERGIES 2020年第12期13卷 3118页

作者： Bi, Qiushi Wang, Guoqiang Wang, Yongpeng Yao, Zongwei Hall, Robert Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China Taiyuan Heavy Ind Co LTD Taiyuan 030024 Peoples R China State Key Lab Min Equipment & Intelligent Mfg Taiyuan 030024 Peoples R China Minist Educ Key Lab CNC Equipment Reliabil Changchun 130025 Peoples R China Univ Alberta Sch Min & Petr Engn Edmonton AB T6G 2H5 Canada

As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of20 sto10 safter the first phase optimization, and the energy consumption per payload reduced by13.28%after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

关键词： digging trajectory cable shovel robotic excavation multi-objective genetic algorithm

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Building performance optimization for university dormitory through integration of digital gene map into multi-objective genetic algorithm

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APPLIED ENERGY 2022年 307卷 118211-118211页

作者： Chi, Fang'ai Xu, Ying Zhejiang A&F Univ Sch Landscape Architecture Hangzhou 311300 Peoples R China

College students spent most of their time in dormitory buildings, which account for a large proportion of the overall electricity use in university. Building performance is determined by its genes (i.e., building component characters), which can be optimized in the design stage. However, a building is comprised of many components, of which some are contradictory to each other in the building performance optimization process. In doing so, a digital gene map was proposed for the university dormitory, characterized by the binary code strings. Based on the digital gene map, the building elements can be parameterized to create some dynamic variables, to facilitate the multi-objective genetic algorithm. Via the multi-objective genetic algorithm and the data statistics tool of Design Explorer in this work, the "Pareto front" solutions can be obtained to optimize the decision-makings in the dormitory building design. For evaluations of the building performance improvement potentials for various types of study rooms with optimized solutions, we conducted the comparison studies in this work. Through comparison studies, we found that the optimized solutions from the multi-objective genetic algorithm, for the nine types of study rooms, have better compromised building performances. The methodology proposed in this work can be applicable for different types of university dormitories under various climate conditions, due to the dormitory buildings have a similar gene pool and chromosomal structure.

关键词： multi-objective genetic algorithm Digital gene map Visual comfort Thermal comfort Energy consumption

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Programming and read performances optimization of phase-change memory via multi-objective genetic algorithm and improved finite element analysis

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MATERIALS SCIENCE IN SEMICONDUCTOR PROCESSING 2024年 169卷

作者： Tang, Pu Tao, Ming Xiao, Jing Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

Phase Change Memory (PCM) has attracted widespread attention for its high-speed reading and writing capabilities and high-density non-volatile storage. However, reducing the reset current of PCM can result in an increase in its set resistance, which affects the programming or readout performances. Therefore, further optimization of PCM under this trade-off remains a significant challenge. In this paper, multi-objective genetic algorithm (MOGA) is utilized to address this problem through the optimization of PCM's geometry. An enhanced PCM finite element model, considering the Seebeck, Peltier, and Thomson effects, is employed as the fitness function of MOGA to improve the accuracy of PCM's reset arguments and obtain precise solution. The PCM geometry solution set with optimal performance in different technology nodes are presented in this paper. Specifically, for the 22-nm node, the optimization result shows that the reset current can be reduced by up to 8.7 % when the PCM is employed for low-power applications, such as neuromorphic computing, and the read latency time can be reduced by up to 22.2 % when the PCM is utilized for fast reading purposes, such as in PCM-DRAM main memory. This proposed optimization scheme has great potential in improving the performance of PCM.

关键词： Phase change memory Reset current Set resistance Thermoelectric effect multi-objective genetic algorithm

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Design Optimization of an Iron Core for Cup-Type AMF Contacts Using genetic Aggregation Response Surface and multi-objective genetic algorithm

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2024年第8期34卷 1页

作者： Huang, Lei Ma, Hui Wang, Zihan Li, Yuanzhao Geng, Yingsan Wang, Jianhua Liu, Zhiyuan Xian Jiao tong Univ XJTU Dept Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China

For cup-type AMF contacts (CACs) in high voltage vacuum interrupters (VIs), the iron core is crucial for enhancing the interrupting capability. The objective of this paper is to employ the genetic Aggregation response surface and multi-objective genetic algorithm for optimizing the AMF characteristics in CACs, which could make the design approach more efficient. Three objectives were chosen for optimization: the highest AMF flux density measured at the central plane within the contact gap (B-AMF), the corresponding phase shift time for B-AMF (t(s)) and the weight of the iron core (m). Finally, an optimal iron core structure was suggested. When comparing the original structure with the optimal one, the AMF in the optimal structure was enhanced across 64.0% of the intermediate plane. Particularly in the central area, the AMF increased by 10.6%. These results demonstrate that significant enhancements were observed in the AMF characteristics of the optimal structure, essential for improving the interrupting capacity of VIs.

关键词： Iron Optimization Response surface methodology Magnetic cores Magnetic fields genetic algorithms Correlation Axial magnetic field cup-type AMF contact design optimization iron core multi-objective genetic algorithm response surface

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Comparing the performance of four shading strategies based on a multi-objective genetic algorithm: A case study in a university library

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JOURNAL OF BUILDING ENGINEERING 2023年 63卷

作者： Wen, Shikang Hu, Xiao Hua, Guanjun Xue, Peng Lai, Dayi Shanghai Jiao Tong Univ Sch Design Dept Architecture Shanghai 200240 Peoples R China Beijing Univ Technol Beijing Key Lab Green Built Environm & Energy Eff Beijing 100124 Peoples R China

Through the control of excessive daylight in buildings, shading devices can reduce glare and improve occupants??? visual comfort. However, shading devices may overly reduce illuminance levels. Many shading strategies are available, but they do not perform equally well, and tradi-tionally, it has been difficult to select the most suitable shading strategy. This study proposed a parameterization method for the selection and design of a shading strategy that would reduce glare while maintaining a satisfactory daylighting level, through the construction of Pareto sets based on a multi-objective genetic algorithm. The objective function was created by combining a dynamic glare evaluation indicator, the spatial glare autonomy (sGA), and a self-constructed daylight index, the spatial daylight vote autonomy (sDVA), developed from a field survey. As a case study, the proposed method was used to compare the performance of four shading strategies, including vertical slats (Vs), a perforated aluminum sheet (PAS), serrated windows with southern orientation (Sw_S), and serrated windows with northern orientation (Sw_N) in a university library in Shanghai, China. It was found that the Sw_S and PAS had a relatively bad performance;the Vs performed better than the Sw_N in providing a more satisfactory illuminance level;and the Sw_N was more effective in reducing the glare. The multi-objective optimization process can be used to obtain near-optimal design parameters that create a visual environment with reduced glare while maintaining an acceptable illuminance level, for all four shading strategies. The developed method can be a helpful tool in the design of an appropriate daylighting environment.

关键词： multi-objective genetic algorithm Shading strategy optimization Daylight environment Glare Visual comfort Pareto set Artificial neural network

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A multi-objective genetic algorithm based approach for dynamical bus vehicles scheduling under traffic congestion

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SWARM AND EVOLUTIONARY COMPUTATION 2020年 54卷 100667-000页

作者： Wang, Chunlu Shi, Hongyi Zuo, Xingquan Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China

Bus vehicle scheduling is very vital for bus companies to reduce operation cost and guarantee quality of service. Many big cities face the problem of traffic congestion, which leads to the planed vehicle scheduling scheme becoming infeasible. It is significant to study bus vehicle scheduling approaches under uncertain environments, such as traffic congestion. In this paper, a bus vehicle scheduling approach is proposed to handle the traffic congestion. It consists of three phases: firstly, a set of candidate vehicle blocks is generated once traffic congestion happens. Secondly, a non-dominated sorting genetic algorithm is adopted to select a subset of vehicle blocks from the set of candidate blocks to generate a set of Non-dominated solutions. Finally, a departure time adjustment procedure is applied to the Non-dominated solutions to further improve the quality of solutions. Experiments on a real-world bus line show that the proposed approach is able to dynamically generate scheduling schemes and significantly improve the quality of service compared to the comparative approaches.

关键词： Bus vehicle scheduling Dynamic vehicle scheduling multi-objective genetic algorithm Urban bus scheduling

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An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the multi-objective genetic algorithm

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PROCESSES 2024年第6期12卷 1251页

作者： Mani, Pemila Komarasamy, Pongiannan Rakkiya Goundar Rajamanickam, Narayanamoorthi Alroobaea, Roobaea Alsafyani, Majed Afandi, Abdulkareem SRM Inst Sci & Technol Dept Elect & Elect Engn Chennai 603203 India SRM Inst Sci & Technol Dept Comp Technol Chennai 603203 India Taif Univ Coll Comp & Informat Technol Dept Comp Sci POB 11099 Taif 21944 Saudi Arabia Univ Business & Technol Gen Subject Dept Jeddah 23435 Saudi Arabia

Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the multi-objective Optimization genetic algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification.

关键词： vehicle classification machine learning eXtreme gradient boost algorithm multi-objective genetic algorithm

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Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron-multi-objective genetic algorithm

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BMC PLANT BIOLOGY 2022年第1期22卷 324-324页

作者： Gammoudi, Najet Nagaz, Kamel Ferchichi, Ali Arid Lands Inst IRA Arid & Oases Cropping Lab Medenine 4119 Tunisia Natl Inst Agron Tunis 43 Charles Nicolle Tunis 1082 Tunisia

Background Contamination-free culture is a prerequisite for the success of in vitro - based plant biotechnology. Aseptic initiation is an extremely strenuous stride, particularly in woody species. Meanwhile, over-sterilization is potentially detrimental to plant tissue. The recent rise of machine learning algorithms in plant tissue culture proposes an advanced interpretive tool for the combinational effect of influential factors for such in vitro - based steps. Results A multilayer perceptron (MLP) model of artificial neural network (ANN) was implemented with four inputs, three sterilizing chemicals at various concentrations and the immersion time, and two outputs, disinfection efficiency (DE) and negative disinfection effect (NDE), intending to assess twenty-seven disinfection procedures of Pistacia vera L. seeds. Mercury chloride (HgCl2;0.05-0.2%;5-15 min) appears the most effective with 100% DE, then hydrogen peroxide (H2O2;5.25-12.25%;10-30 min) with 66-100% DE, followed by 27-77% DE for sodium hypochlorite (NaOCl;0.54-1.26% w/v;10-30 min). Concurrently, NDE was detected, including chlorosis, hard embryo germination, embryo deformation, and browning tissue, namely, a low repercussion with NaOCl (0-14%), a moderate impact with H2O2 (6-46%), and pronounced damage with HgCl2 (22-100%). Developed ANN showed R values of 0.9658, 0.9653, 0.8937, and 0.9454 for training, validation, testing, and all sets, respectively, which revealed the uprightness of the model. Subsequently, the model was linked to multi-objective genetic algorithm (MOGA) which proposed an optimized combination of 0.56% NaOCl, 12.23% H2O2, and 0.068% HgCl2 for 5.022 min. The validation assay reflects the high utility and accuracy of the model with maximum DE (100%) and lower phytotoxicity (7.1%). Conclusion In one more case, machine learning algorithms emphasized their ability to resolve commonly encountered problems. The current successful implementation of MLP-MOGA inspires its application for more

关键词： In vitro culture Artificial neural network multi-objective genetic algorithm Optimization Disinfection Pistacia vera

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Optimization of multi-Mission CubeSat Constellations with a multi-objective genetic algorithm

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REMOTE SENSING 2023年第6期15卷 1572-1572页

作者： Melaku, Shimeles Demissie Kim, Hae-Dong Univ Sci & Technol Aerosp Syst Engn Daejeon 34113 South Korea Gyeongsang Natl Univ Dept Aerosp & Software Engn Jinju 52828 South Korea

The increasing demand for low-cost space-borne Earth observation missions has led to small satellite constellation systems development. CubeSat platforms can provide a cost-effective multiple-mission space system using state-of-the-art technology. This paper presents a new approach to CubeSat constellation design for multiple missions using a multi-objective genetic algorithm (MOGA). The CubeSat constellation system is proposed to perform multi-missions that should satisfy global Earth observation and regional disaster monitoring missions. A computational approach using a class of MOGA named non-dominated sorting genetic algorithm II is implemented to optimize the proposed system. Pareto optimal solutions are found that can minimize the number of satellites and the average revisit time (ART) for both regional and global coverage while maximizing the percentage coverage. As a result, the study validates the feasibility of implementing the CubeSat constellation design with an acceptable level of performance in terms of ART and percentage coverage. Moreover, the study demonstrates CubeSat's ability to perform a multi-missions.

关键词： CubeSat multi-mission constellation multi-objective genetic algorithm

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