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A multi-objective structural optimization method for serviceability design of tall buildings

STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2023年第17期32卷

作者： Huang, Ming-Feng Wang, Chun-He Lin, Wei Xiao, Zhi-Bin Zhejiang Univ Inst Struct Engn Hangzhou Peoples R China Zhejiang Univ Ctr Balance Architecture Hangzhou Peoples R China Zhejiang Univ Inst Struct Engn Hangzhou 310058 Peoples R China

Structural optimization design aims to identify optimal design variables corresponding to a minimum objective function with constraints on performance requirements. To this end, many optimization frameworks have been proposed to determine optimal structural systems that are subjected to seismic and wind hazards in isolation. However, some modern tall buildings are sensitive to seismic and wind excitation owing to their complex structural systems and geographic regions. Therefore, a proper structural optimization method for such buildings is required to ensure that the expected performance is achieved in a multi-hazard scenario. This study proposes a multi-objective serviceability design optimization methodology for buildings in multi-hazard seismic and wind environments by combining optimality criteria and the nondominated sorting genetic algorithm ii (NSGA-ii). Seismic and wind effects can be instantaneously updated due to changes in the structural dynamic properties during the optimal design process. A neural-network-based surrogate model with self-updating is proposed to predict the structural natural frequency so that the overall computation time of the optimization process can be reduced. The proposed method was used to optimize a 50-story frame-tube building and was compared against the general genetic algorithm and general NSGA-ii to verify the feasibility and effectiveness.

关键词： multi-hazard environment multi-objective optimization nondominated sorting genetic algorithm ii optimality criteria serviceability design optimization surrogate model

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Multi-Objective Optimization of Laminated Functionally Graded Carbon Nanotube-Reinforced Composite Plates Using Deep Feedforward Neural Networks-NSGAii algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS 2022年第3期19卷

作者： Ly, Duy K. Truong, Tam T. Nguyen-Thoi, T. Ton Duc Thang Univ Inst Computat Sci Div Computat Math & Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam

This paper proposes a novel and efficient DNN-NSGAii approach which is an integration of the deep feedforward neural network (DNN) and the nondominated sorting genetic algorithm ii (NSGAii) to solve multi-objective optimization (MOO) problems of laminated functionally graded carbon nanotube-reinforced composite (FG-CNTRC) quadrilateral plates. The core idea of the proposed approach is to use the DNN as an analyzer to evaluate the objective and constraint functions instead of using the time-consuming finite element analysis methods (FEAMs) during the optimization process, while the NSGAii is employed to find a set of Pareto-optimal solutions of the MOO problems. Accordingly, the proposed DNN-NSGAii remarkably saves the computational cost, but still provides a high accurate optimal solution. The precision, efficiency, and capability of the proposed method are demonstrated through two different MOO problems of the FG-CNTRC quadrilateral plates. Obtained results of the DNN-NSGAii are compared with those of other methods to investigate the reliability and efficiency of proposed method. Moreover, the effects of various boundary conditions and carbon nanotube (CNT) distributions on the Pareto-optimal solutions of MOO problems are also examined.

关键词： Laminated functionally graded carbon nanotube-reinforced composite quadrilateral plates multi-objective optimization deep feedforward neural network nondominated sorting genetic algorithm ii

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Research on capacity and strategy optimization of combined cooling, heating and power systems with solar photovoltaic and multiple energy storage

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ENERGY CONVERSION AND MANAGEMENT 2022年 268卷

作者： Wang, Zhi Cai, Wenkui Tao, Hongjun Wu, Di Meng, Jinxiang North China Elect Power Univ Sch Energy Power & Mech Engn Dept Power Engn Baoding 071003 Peoples R China

Combined cooling, heating and power systems can exploit renewable energy and significantly improve the comprehensive benefit of energy supply systems. Moreover, the stability and economy of these systems are mainly affected by the operation strategy and the capacity allocation scheme of each device. Therefore, a novel hourly dynamic simulation model is established to obtain the optimal capacity allocation scheme. The novelty is that it considers the reduction in CO2, NOx and SO2 emissions. The variable load operation characteristics of the main devices are considered. With the operation strategy of following the electrical load and following the thermal load, the model is solved by nondominated sorting genetic algorithm ii with the optimization objectives, including economy, environment and energy savings. The technique for order preference by similarity to an ideal solution method is used as a multiobjective decision method. As a result, with the following the electrical load strategy, the pollutant emission reduction rate is 74.86%, the primary energy savings rate is 36.83%, the annual cost savings rate is 13.04%, and the comprehensive index is 54.89%. When the electrical cooling ratio is 0.8, it is 0.29% higher than the pollutant emission reduction rate without mixed refrigeration and 0.63% higher than the primary energy savings rate. When the optimal capacity of the photovoltaic system is configured, the pollutant emission reduction rate increases by 5.13%, the primary energy savings rate increases by 15.59%, the annual cost savings rate increases by 7.48%, and the comprehensive index increases by 7.51%. Therefore, the novel model and optimization algorithm are reliable and feasible for addressing the system capacity configuration improving system operation stability.

关键词： Combined cooling heating and power system Capacity optimization Operation strategy nondominated sorting genetic algorithm ii Solar photovoltaic Energy storage technology

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A stochastic multi-objective optimization decision model for energy facility allocation: a case of liquefied petroleum gas station

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2020年第2期22卷 389-398页

作者： Yang, Ming-Der Chen, Yi-Ping Wang, Chien-Tsung Deng, Ming-Jay Lin, Yu-Hao Chen, Ho-Wen Natl Chung Hsing Univ Innovat & Dev Ctr Sustainable Agr Dept Civil Engn 145 Xingda Rd Taichung 402 Taiwan Pervas AI Res PAIR Labs 1001 Univ Rd Hsinchu 300 Taiwan Da Yeh Univ Dept Business Adm 168 Univ Rd Dacun 515 Changhua Taiwan Natl Yunlin Univ Sci & Technol Dept Chem & Mat Engn 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Natl Yunlin Univ Sci & Technol Bachelor Program Interdisciplinary Studies 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Tunghai Univ Dept Environm Sci & Engn 181 Sec 3Taichung Port Rd Taichung 407 Taiwan

To mitigate air pollution problem, the government has been planning to build more liquefied petroleum gas stations to motivate drivers to use liquefied petroleum gas vehicles in Taiwan. Such facility allocation problem is a multi-objective optimization process considering spatial variation in the need of refueling. This study presents a stochastic multi-objective optimization model for liquefied petroleum gas station allocation (SMOMLSA) that integrates a nondominated sorting genetic algorithm ii with a Monte Carlo simulation to optimally allocate liquefied petroleum gas stations according to three trade-off objectives, including investment performance, energy conversion, and business opportunity. Monte Carlo simulation procedure generates the starting location of a taxicab car in need of refueling in the spatial grid based on a probability distribution. nondominated sorting genetic algorithm ii resolves the station location problem with these multi-objectives. The SMOMLSA was validated by conducting a real-world case study. Result depicts that the SMOMLSA can provide information on the optimal allocation of liquefied petroleum gas stations for minimizing construction costs, minimizing average refueling distance for vehicles, and maximizing potential customers. [GRAPHICS] .

关键词： Monte Carlo simulation (MCS) Stochastic multi-objective optimization nondominated sorting genetic algorithm ii Alternative energy

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NSGA-ii with objective-specific variation operators for multiobjective vehicle routing problem with time windows

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 176卷 114779-114779页

作者： Srivastava, Gaurav Singh, Alok Mallipeddi, Rammohan Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Telangana India Kyungpook Natl Univ Sch Elect Engn Deaprtment Artificial Intelligence Daegu 41566 South Korea

Vehicle routing problem with time windows (VRPTW) is a pivotal problem in logistics domain as it possesses multiobjective characteristics in real-world applications. Literature contains a general multiobjective VRPTW (MOVRPTW) with five objectives along with MOVRPTW benchmark instances that are derived from real-world data. In this paper, we have proposed a nondominated sorting genetic algorithm ii (NSGA-ii) based approach with objective-specific variation operators to address the MOVRPTW. In the proposed NSGA-ii approach, the crossover and mutation operators are designed by exploiting the problem characteristics as well as the attributes of each objective. The performance of the proposed approach is evaluated on the standard benchmark instances of the problem and compared with the state-of-the-art approach available in literature. The computational results demonstrate the superiority of our approach over the state-of-the-art approach for the MOVRPTW.

关键词： Evolutionary algorithms Multiobjective optimization nondominated sorting genetic algorithm ii Vehicle routing problem with time windows

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Modeling and coordinated optimization method featuring coupling relationship among subsystems for improving safety and efficiency of drilling process

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APPLIED SOFT COMPUTING 2021年 99卷 106899-106899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 C Wuhan 430074 Peoples R China

Drilling is an important means of obtaining resources. Since the complicated bottom hole conditions and coupling relationship among subsystems, it is difficult to accurately predict the drilling states and simultaneously improve drilling efficiency and safety. A novel modeling and coordinated optimization method has been developed to solve that. In the modeling part, based on the analysis of the correlation between subsystems, two online support vector regression (OSVR) models are established to predict drilling states of rate of penetration (ROP) and mud pit volume (MPV) respectively. In the optimization part, based on the prediction models, the problem of how to improve drilling efficiency and safety is described into a two-objective optimization issue, which includes improving ROP and maintaining MPV at same time. Then a coordinated optimization strategy is developed to finish it, which adopts the nondominated sorting genetic algorithm ii (NSGA-ii) to find out pareto set, and the technique for order preference by similarity to an ideal solution (TOPSIS) method to determine the final optimal operating parameters. Finally, the verification of modeling and coordinated optimization method based on the actual drilling data show that it can improve ROP by an average of 39.8% and reduce the fluctuation of the MPV by an average of 69.3%, which demonstrates effectiveness and practicability for our method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Rate of penetration Mud pit volume Modeling and coordinated optimization method Online support vector regression nondominated sorting genetic algorithm ii

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Parameter design method for capacitive power and signal transfer system based on multi-objective optimisation algorithm

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IET POWER ELECTRONICS 2019年第10期12卷 2650-2659页

作者： Su, Yu-Gang Zhou, Wei Wang, Zhi-Hui Wu, Xue-Ying Qing, Xiao-Dong Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ 174 Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Sch Automat 174 Shazheng St Chongqing 400044 Peoples R China

For most capacitive power transfer applications, a reliable signal transfer link between the primary side and the secondary side plays an essential role. The parameters design of the system has a key effect on the operating performance, however, the existing parameter design method can only get an acceptable solution, instead of a globally optimal solution. This study has proposed a general and global optimal parameter design method based on the constraint multi-objective algorithm. The mathematical models of the power and signal channels as long as the power noise is established and all the objective functions are given according to the models. Besides, the constraints considering the reasonable region of the parameter values and the limitation of the voltage and current on the elements are given. Then the multi-objective optimisation problem with the objective functions and constraints are solved by the non-dominated sorting genetic algorithm ii, and the proper global optimal parameters are selected from the Pareto set. Finally, a simulation model and an experimental setup with 180 W power and 200 kbps signal transfer capability are constructed, and the results verified the effectiveness and correctness of this method.

关键词： genetic algorithms Pareto optimisation sorting inductive power transmission signal transfer system multiobjective optimisation algorithm capacitive power transfer applications reliable signal transfer link globally optimal solution general parameter design method global optimal parameter design method constraint multiobjective algorithm mathematical models signal channels power noise objective functions parameter values multiobjective optimisation problem nondominated sorting genetic algorithm ii global optimal parameters signal transfer capability Pareto set power 180 0 W bit rate 200 kbit s

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Centralised multi-objective integration of wind farm and battery energy storage system in real-distribution network considering environmental, technical and economic perspective

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第22期13卷 5207-5217页

作者： Ahmadi, Mikaeel Lotfy, Mohammed Elsayed Howlader, Abdul Motin Yona, Atsushi Senjyu, Tomonobu Univ Ryukyus Fac Engn 1 Senbaru Nishihara Okinawa 9030213 Japan Zagazig Univ Fac Engn Zagazig 44511 Egypt Univ Hawaii Hawaii Nat Energy Inst Honolulu HI 96822 USA

Integration of renewable energies such as wind and solar with an energy storage system (ESS) in a distribution network is the interest of current studies in power system engineering. Wind and battery ESS (BESS) are known for their complement and efficient approaches into the distribution networks. The promising of renewable energies for wind and solar in Afghanistan is a motivation for stepping up the power sector of the country by enhancing the power quality as well as self-dependency in electricity production. In this study, a multi-objective optimisation technique, non-dominated sorting genetic algorithm ii (NSGA-ii) is proposed for an extensive distribution network in Kabul city considering technical, environmental, and financial control schemes for the network improvement. Three different scenarios with various objective functions are deemed to evaluate their impact on decision variables and network parameters. Furthermore, optimum allocation of the wind turbine and charge/discharge scheduling of BESS are revealed with improvement in performance of the power system. Simulations are deployed in MATLAB (R) with its application on developed 162-bus real-distribution network to demonstrate the effect of different objective function arrangements in each scenario as well as confirming the robustness of the proposed approach.

关键词： distribution networks genetic algorithms battery storage plants wind turbines wind power plants power supply quality power generation economics power generation scheduling solar power stations multiobjective integration wind farm battery energy storage system real-distribution network renewable energies ESS power system engineering BESS power sector power quality multiobjective optimisation technique nondominated sorting genetic algorithm ii extensive distribution network technical control schemes financial control schemes network improvement network parameters wind turbine objective function arrangements battery ESS electricity production decision variables optimum allocation charge-discharge scheduling Matlab solar energy

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Data-Driven Energy Efficiency and Part Geometric Accuracy Modeling and Optimization of Green Fused Filament Fabrication Processes

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JOURNAL OF MECHANICAL DESIGN 2020年第4期142卷 1-9页

作者： Alizadeh, Morteza Esfahani, Mehrnaz Noroozi Tian, Wenmeng Ma, Junfeng Mississippi State Univ Dept Ind & Syst Engn Starkville MS 39762 USA

Nowadays, increasing awareness of environmental protection has evoked the adoption of green technologies in design and manufacturing. As a revolutionizing manufacturing technology that produces components in a layer-by-layer fashion, additive manufacturing (AM) has followed this trend. Among a variety of AM processes, fused filament fabrication (FFF) is one of the most commonly used technologies. However, AM (including FFF) is inherently energy expensive and energy inefficient compared with the conventional manufacturing. Thus, an urgent investigation is needed to reduce the energy consumption for AM production. On the other hand, part geometric accuracy is an important aspect for the quality of additively manufactured components. It is not meaningful to improve AM's energy consumption performance with compromised part geometric accuracy. Therefore, it is necessary to jointly consider energy consumption as well as part geometric accuracy in the AM process design. This study applies the statistical regression approach to model AM energy consumption and part geometric accuracy. The nondominated sorting genetic algorithm ii (NSGA-ii) and the technique for order of preference by similarity to ideal solution (TOPSIS) method together are used to locate the compromised optimal solution for AM process parameter settings. The effectiveness of the proposed approach is demonstrated through a case study developed with the FFF process and a specific part design. The results of this study are significant to both AM energy consumption and part geometric accuracy in terms of qualitative and quantitative analyses. Furthermore, the study can potentially guide the future AM sustainability model development and be extended to future AM process improvement.

关键词： additive manufacturing energy consumption nondominated sorting genetic algorithm ii part geometric accuracy design methodology design optimization multi-objective optimization

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A Multiobjective Optimization Approach for Extended Interaction Oscillators

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IEEE ACCESS 2020年 8卷 166226-166235页

作者： Cui, Jian Bian, Shurong Li, Aidi North China Univ Technol Informat Sch Beijing 100144 Peoples R China

The development of high-frequency, wide-bandwidth, high-power extended interaction oscillators (EIOs) has always been the focus of researchers working on millimeter-wave and terahertz electronic devices. However, these design objectives are affected by many structural and operating parameters, and traditional manual optimization and local optimization are no longer suitable for solving these problems. In this paper, based on a one-dimensional, nonlinear, self-consistent program of EIOs, a multiobjective optimization method that employs the nondominated sorting genetic algorithm ii (NSGA-ii) is proposed to simultaneously optimize device output power, bandwidth, and structure length. By using this approach, the optimization process of a 95-GHz EIO is presented, and the corresponding Pareto solutions are obtained after 500 generations with a population size of 50. The results show that the beam-wave interaction and the coupling mechanism lead to synchronization of the structural parameters and the electrical parameters with each other, and the coexistence of multiple objectives guides the zonal distribution of the optimal solutions. That is, the oscillators with fewer gaps have shorter structure length and higher power, whereas those with more gaps are prone to start oscillation and have wider bandwidth. Several sets of optimization results obtained using the proposed method agree well with the results obtained in the CST-PIC solver, which proves that the proposed algorithm is effective for optimizing EIOs because it considers multiple design goals and can serve as a theoretical basis for engineering development.

关键词： Optimization genetic algorithms Power generation Electron beams Bandwidth Oscillators Klystrons Extended interaction oscillators nonlinear self-consistent theory nondominated sorting genetic algorithm ii multiobjective optimization

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