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multiobjective optimization of an industrial grinding operation using elitist nondominated sorting genetic algorithm

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CHEMICAL ENGINEERING SCIENCE 2004年第2期59卷 385-396页

作者： Mitra, K Gopinath, R Tata Consultancy Serv Pune 411013 Maharashtra India Tata Consultancy Serv Bombay 400021 Maharashtra India

The elitist version of nondominated sorting genetic algorithm (NSGA II) has been adapted to optimize the industrial grinding operation of a lead-zinc ore beneficiation plant. Two objective functions have been identified in this study: (i) throughput of the grinding operation is maximized to maximize productivity and (ii) percent passing of one of the most important size fractions is maximized to ensure smooth flotation operation following the grinding circuit. Simultaneously, it is also ensured that the grinding product meets all other quality requirements, to ensure least possible disturbance in the following flotation circuit, by keeping two other size classes and percent solid of the grinding product and recirculation load of the grinding circuit within the user specified bounds (constraints). Three decision variables used in this study are the solid ore flowrate and two water flowrates at two sumps, primary and secondary, each of them present in each of the two stage classification units. Nondominating (equally competitive) optimal solutions (Pareto sets) have been found out due to conflicting requirements between the two objectives without violating any of the constraints considered for this problem. Constraints are handled using a technique based on tournament selection operator of genetic algorithm which makes the process get rid of arbitrary tuning requirement of penalty parameters appearing in the popular penalty function based approaches for handling constraints. One of the Pareto points, along with some more higher level information, can be used as set points for the previously mentioned two objectives for optimal control of the grinding circuit. Implementation of the proposed technology shows huge industrial benefits. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： dynamic simulation mathematical modeling multiobjective optimization genetic algorithm Pareto set

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multiobjective optimization of Safety, Comfort, Fuel Economy, and Power Sources Durability for FCHEV in Car-Following Scenarios

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2023年第1期9卷 1797-1808页

作者： Zhu, Longlong Tao, Fazhan Fu, Zhumu Sun, Haochen Ji, Baofeng Chen, Qihong Henan Univ Sci & Technol Coll Informat Engn Luoyang 471000 Peoples R China Henan Univ Sci & Technol Henan Key Lab Robot & Intelligent Syst Luoyang 471000 Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

Safety, comfort, and energy-saving oriented carfollowing issue for fuel cell/battery hybrid electric vehicle (FCHEV) is a comprehensive problem of vehicle dynamic and fuel economy. Combining adaptive cruise control (ACC) and energy management strategy (EMS) is proven to be one promising method to realize multiobjective co-optimization. However, in majority of existing researches, degradation of power sources is always ignored and trade-off among different objectives remains a burning issue, in this article, a Pareto-based EMS under car-following scenarios is proposed. Specifically, degradation models of fuel cell/battery are established and incorporated into multiobjective optimization functions. Then, based on back-stepping technique and equivalent consumption minimization strategy (ECMS), an integrated framework of ACC and EMS is developed, which realizes the coordination between vehicle external longitudinal dynamics control and internal powertrain energy management. For getting the trade-off among the abovementioned objectives, a Pareto-involved multiobjective optimization method is proposed to optimize control parameters of the integrated framework of ACC and EMS. The simulation results of Urban Dynamometer Driving Schedule (UDDS) test highlight that compared with the weighted-sum methods, the proposed method can utmost reduce average tracking error by 15.14%, variation of speed by 8.61%, equivalent fuel consumption cost by 7.56%, and comprehensive power sources degradation cost by 17.78%.

关键词： Fuel cells Energy management Degradation Hybrid electric vehicles Vehicle dynamics Batteries optimization Adaptive cruise control (ACC) degradation of power sources energy management strategy (EMS) fuel cell battery hybrid electric vehicle (FCHEV) multiobjective optimization

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multiobjective optimization of Multistage Synchronous Induction Coilgun Based on NSGA-II

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IEEE TRANSACTIONS ON PLASMA SCIENCE 2017年第7期45卷 1622-1628页

作者： Niu, Xiaobo Liu, Kaipei Zhang, Yadong Xiao, Gang Gong, Yujia Wuhan Univ Sch Elect Engn Wuhan 430072 Peoples R China

The structure and trigger control strategy have become the most important factors that restrict the performance of the multistage synchronous induction coilgun (MSSICG). However, it is still a difficult task to design MSSICG under overload constraint due to coupling between the multiple parameters. In this paper, the maximization of the emission efficiency and acceleration stationarity is treated as a multiobjective optimization problem. By analyzing the relationship between the number of turns and the other structural parameters of the launch, the multiobjective optimization model of MSSICG is established by the current filament method which was verified by the experimental data and finite-element method. And then the second generation nondominated sorting genetic algorithm (NSGA-II) and multiobjective particle swarm optimization (MOPSO) were employed to optimize the model in order to maximize the energy transfer efficiency while achieving the smooth acceleration of the armature. With the formulated optimization model, a five-stage synchronous induction coilgun is optimized as a special case. A decision-making procedure based on the fuzzy membership function is used for obtaining best compromise solution from the set of Pareto-solutions obtained through NSGA-II and MOPSO. In addition, the optimization performance of the proposed multiobjective optimization model and the single-objective optimization model of the MSSICG was compared. The result of optimization shows that the proposed multiobjective optimization model of MSSICG can effectively improve the performance of the coilgun compared with the single-objective optimization model which takes of the launch velocity and overload acceleration as the combination objective function or only the launch velocity.

关键词： Current filament method (CFM) fuzzy membership function (FMF) multiobjective optimization multiobjective particle swarm optimization (MOPSO) multistage synchronous induction coilgun (MSSICG) nondominated sorting genetic algorithm-II (NSGA-II)

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multiobjective optimization based on polynomial chaos expansions in the design of inductive power transfer systems

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2022年第6期41卷 2045-2059页

作者： Pei, Yao Pichon, Lionel Bensetti, Mohamed Le Bihan, Yann Univ Paris Saclay Lab Genie Elect & Elect Paris CNRS Cent Supelec Gif Sur Yvette France Sorbonne Univ Lab Genie Elect & Elect Paris CNRS Paris France

Purpose - The purpose of this study is to decrease the computation time that the large number of simulations involved in a parametric sweep when the model is in a three-dimensional environment. Design/methodology/approach - In this paper, a new methodology combining the PCE and a controlled, elitist genetic algorithm is proposed to design IPT systems. The relationship between the quantities of interest (mutual inductance and ferrite volume) and structural parameters (ferrite dimensions) is expressed by a PCE metamodel. Then, two objective functions corresponding to mutual inductance and ferrite volume are defined. These are combined together to obtain optimal parameters with a trade-off between these outputs. Findings - According to the number of individuals and the generations defined in the optimization algorithm in this paper, it needs to calculate 20,000 times in a 3D environment, which is quite time-consuming. But for PCE metamodel of mutual inductance M, it requires at least 100 times of calculations. Afterward, the evaluation of M based on the PCE metamodel requires 1 or 2 s. So compared to a conventional optimization based on the 3D model, it is easier to get optimized results with this approach and it saves a lot of computation time. Originality/value - The multiobjective optimization based on PCEs could be helpful to perform the optimization when considering the system in a realistic 3D environment involving many parameters with low computation time.

关键词： Inductive power transfer multiobjective optimization Polynomial chaos expansions Surrogate optimization

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multiobjective optimization using Mesh Adaptive Direct Search for power dispatch problem of microgrid

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期42卷 728-735页

作者： Mohamed, Faisal A. Koivo, Heikki N. Omar Al Mukhtar Univ Dept Elect Engn El Bieda Libya Aalto Univ Dept Automat & Syst Technol Aalto 00076 Finland

This paper presents a generalized formulation for determining the optimal operating strategy and cost optimization scheme and reduction of emissions of a MicroGrid (MG). multiobjective (MO) optimization is applied to the environmental economic problem of the MG. The proposed problem is formulated as a nonlinear constrained MO optimization problem. The proposed problem takes into consideration the operation and maintenance costs as well as the emission reduction of NOx, SO2, and CO2. The MG considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array, a fuel cell, and a battery storage. The multiobjective Mesh Adaptive Direct Search (MOMADS) is employed to minimize the cost function of the system while constraining it to meet the costumer demand and safety of the system. A comparison is made with multiobjective Sequential Quadratic Programming (MOSQP). The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions. (c) 2011 Elsevier Ltd. All rights reserved.

关键词： Microgrid multiobjective optimization Load management Mesh Adaptive Direct Search

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multiobjective optimization for Heat Transfer Performance of Micro-Fins and Signal Integrity of Key Interconnect Technologies in 3-D Integrated Chips

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IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY 2022年第8期12卷 1339-1349页

作者： He, Wei Yin, Ershuai Qiang, Li Nanjing Univ Sci & Technol Sch Energy & Power Engn MIIT Key Lab Thermal Control Elect Equipment Nanjing 210094 Peoples R China

This article is devoted to the synergistic design of the thermal management and signal integrity for 3-D integrated chips. The structure parameters of the micro-fin and interconnected components are optimized to obtain a balanced performance of chip heat dissipation and signal integrity. The 3-D integrated chip flows heat transfer model under the action of electrothermal coupling is developed. A full-wave electromagnetic simulation model of the signal interconnection components is proposed with the same structural parameters. The effects of various structural factors on the heat transfer performance and signal integrity of 3-D integrated chips are investigated. A multiobjective optimization algorithm is carried out to simultaneously reduce the total thermal resistance of the 3-D integrated chip and the signal transmission losses of the critical interconnect components. The results show that the total thermal resistance of the 3-D IC becomes lower with a larger through-silicon via (TSV) diameter, micro-fins height and micro-fin radius, and smaller oxide thickness. When the TSV diameter and oxide thickness increase, the height and radius of the micro-fins are reduced to facilitate the signal transmission of the chip's interconnected components. The response surface analysis method was used to design the experimental combination and thus obtain the fit function for the performance of the two objectives. Hundred sets of Pareto optimal solutions are obtained by multiobjective optimization. The technique for order preference by similarity to ideal solution (TOPSIS) method is used to select the weights of the two optimization objectives to determine the optimal solution. When thermal resistance and signal transmission are given equal weight in the chip design, thermal resistance is reduced by 13.8%, and signal transmission efficiency is improved by 9.74%. The study has important implications for chip thermal management and signal integrity.

关键词： Through-silicon vias Heat transfer Three-dimensional displays Thermal management Signal integrity Heat sinks Heating systems 3-D integrated chips electrothermal coupling multiobjective optimization signal integrity thermal management through-silicon via (TSV)

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multiobjective optimization of a sugarcane biorefinery involving process and environmental aspects

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RENEWABLE ENERGY 2016年 96卷 1142-1152页

作者： Borba Costa, Caliane Bastos Potrich, Erich Goncalves Cruz, Antonio Jose Fed Univ Sao Carlos UFSCar Chem Engn Grad Program Chem Eng Dept UFSCar Rodovia Washington LuisKm 235 BR-13565905 Sao Carlos SP Brazil Fed Univ Sao Carlos UFSCar Dept Chem Engn UFSCar Rodovia Washington LuisKm 235 BR-13565905 Sao Carlos SP Brazil

Process modeling and simulation are essential to predict process energetic demands, and determine possible throughputs and process emissions in biorefineries. In this paper non-linear multiobjective optimization studies with conflicting process and environmental objectives are performed in a sugarcane biorefinery. The process produces bioethanol (first and second generation), bioelectricity and concentrates vinasse in multiple-effect evaporators. Objective functions, concerning process and environmental issues, are defined and studied in five optimization problems. Decision variables are fraction of bagasse diverted to E2G production (Var1), and the fraction of vinasse that is concentrated up to 7.0 degrees Bx in multiple-effect evaporator (Var2). The results show that Varl cannot assume values greater than circa 0.5, due to thermal demands of the integrated process, while Var2 cannot be lower than 0.15, due to process demands for vegetal steam. Flows of concentrated vinasse are generally higher when maximization of bioethanol throughputs is imposed. When electric power generation and bioethanol throughput maximization are the objectives, the former varies 45.6%, while the latter varies 16.6%, among non-dominated solutions. The adopted approach can be used as a decision-making tool that may help to choose suitable operating conditions, in order to obtain a trade-off between greater profits and a more sustainable process. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： multiobjective optimization Ethanol Electric energy Vinasse concentration CO2 emission Sugarcane biorefinery

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multiobjective optimization and Multiphysics Design of a 5 MW High-Speed IPMSM Used in FESS Based on NSGA-II

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2023年第2期38卷 813-824页

作者： Zang, Baiqi Chen, Yiguang Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

The flywheel energy storage system (FESS) has high-power density and fast response speed, hence it is widely used as a pulse power supply. For the high-speed permanent magnet synchronous machine (HSPMSM) used in high-power FESS, there are two technical problems that need to be solved, including huge centrifugal forces of the rotor at a high-speed operation region and large phase currents at a low-speed operation region. In this paper, a 5 MW high-speed interior PMSM (HSIPMSM) is designed for FESS. The design goal of this electric machine is to reduce the maximum phase current at the lowest speed as much as possible while ensuring the rotor mechanical strength. Based on the response surface (RS) model and non-dominated sorting genetic algorithm II (NSGA-II), the multiobjective design of the HSIPMSM is accomplished using 29 groups of multiphysical field finite element analysis (FEA) sampling experiments. The computational cost is effectively reduced. In addition, the constant power optimal trajectory and control switching speed of the HSIPMSM are analyzed and discussed, and the reason that the constant power flux weakening region of the designed HSIPMSM is not wide and the maximum torque per ampere (MTPA) mode is adopted at a low-speed operation region is further illustrated.

关键词： Flux weakening flywheel energy storage system high-speed interior permanent magnet synchronous machine multiobjective optimization rotor mechanical strength

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multiobjective optimization of ceramic-metal functionally graded plates using a higher order model

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COMPOSITE STRUCTURES 2018年第1期183卷 146-160页

作者： Franco Correia, Victor M. Aguilar Madeira, J. F. Araujo, Aurelio L. Mota Soares, Cristovao M. Univ Lisbon Inst Super Tecn IDMEC Av Rovisco Pais P-1049001 Lisbon Portugal Escola Super Naut Infante D Henrique Av Eng Bonneville Franco P-2770058 Paco De Arcos Portugal IPL ISEL Dept Math Rua Conselheiro Emidio Navarro P-1949014 Lisbon Portugal

A methodology of multiobjective design optimization of ceramic-metal composite plates with functionally graded materials, with properties varying through the thickness direction, obtained by an adequate variation of volume fractions of the constituent materials, is presented in this paper. Constrained optimization is conducted for different behavior objectives like the maximization of buckling load or fundamental natural frequency. Mass minimization and material cost minimization are also considered. The optimization problems are constrained by stress based failure criteria and other structural response constraints or manufacturing limitations. The design variables are the index of the power-law distribution in the metal-ceramic graded material and the thicknesses of the graded material and/or the metal and ceramic faces. An equivalent single layer finite element plate model having a displacement field based on a higher order shear deformation theory, accounting for the temperature dependency of the material properties, was developed and validated for the analysis of through-the-thickness ceramic-metal functionally graded plates. The optimization problems are solved with two direct search derivative-free algorithms: GLODS (Global and Local optimization using Direct Search) and DMS (Direct MultiSearch). DMS, the multiobjective optimization solver, is started from a set of local minimizers which are initially determined by the global optimizer algorithm GLODS for each one of the objective functions. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： multiobjective optimization Direct MultiSearch Higher order shear deformation theory Composites Functionally graded materials FGM plates

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multiobjective optimization Approach for Sensor Arrangement in A Complex Indoor Environment

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2012年第2期42卷 174-186页

作者： Lee, Joon-Yong Seok, Joon-Hong Lee, Ju-Jang Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Taejon 305701 South Korea

Various meta-heuristic search methods have been employed to resolve the sensor arrangement problem, which is a type of NP-hard, combinational problem. However, the difficulty of weight tuning, when formulating a single objective function, is the chief obstacle to the use of the single-objective optimization methods. Although multiobjective optimization methods have been applied recently to avoid the difficulty involved in weight design, the original multiobjective optimization method still requires a greater number of generations for the solutions to converge to the optimal Pareto front. Moreover, unlike in previous works, we deal with four unknowns to define the sensor arrangement problem more practically: 1) The number of sensors is unknown, 2) no candidate is given for installation, 3) the coverage radii of sensors are variable, and 4) sensors cover a wide area in which obstacles exist in complicated arrangements. To improve the search approach for a sensor arrangement with these requirements, we first propose a representation scheme to encode the sensor arrangement problem as a set of chromosomes. Genetic operators and a repair scheme are also properly employed in the proposed encoding method. In addition, two strategies, i.e., the hierarchical fitness assignment strategy and the hybrid optimization strategy, are proposed to improve convergence. We also perform experiments with two commercial sensors to verify the proposed multiobjective optimization approach for sensor arrangement (MOASA). The results show that the proposed MOASA gives better performance than conventional search methods. The effects of the proposed strategies are investigated with additional experiments in terms of the quality of Pareto solutions.

关键词： multiobjective optimization Pareto front sensor arrangement

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