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JOURNAL OF ENGINEERING RESEARCH 2025年第1期13卷 267-282页

作者： Kelsy, Braide T. Nwobi-Okoye, Chidozie Chukwuemeka Ezechukwu, Vincent Chukwuemeka Uche, Remy Chukwuemeka Odumegwu Ojukwu Univ Dept Mech Engn Anambra Nigeria Chukwuemeka Odumegwu Ojukwu Univ Fac Engn Engn Anal & Syst Engn Unit Anambra Nigeria Fed Univ Technol Owerri Dept Mech Engn Ihiagwa Nigeria

Environmental pollution is a big problem all over the world. One of the strategies to reduce the menace environmental pollution is the development of the concept of circular economy whose major thrust is the concept of conversion of waste to wealth. The aim of this research is to optimize the production of a new nanocomposite material developed from Al-Si-Mg Alloy derived from waste beverage cans, carbon nanotubes (CNTs) derived from rice husks and nanoparticles from periwinkle shells. The multi-objective optimization of the porosity, hardness and compressive strength of the novel composite was done using the Taguchi method, Artificial Neural Network (ANN), and Nondominated Sorting Genetic Algorithm-II (NSGA-II). The ANN utilized the following input parameters: Weight percentage (wt%) of CNTs, Weight percentage (wt%) of PWSnp, stirring speed (rpm) and Stirring time (minutes). The outputs predicted by the ANN were: Porosity, Hardness, Compressive strength. To achieve optimal porosity, hardness, and compressive strength, the Taguchi-grey relational methodology was employed to simultaneously optimize the production parameters of the composite. The optimal values determined were as follows: 1.5 wt% of CNTs, 1.0 wt% of PWSnp, 100 rpm stirring speed, and 6 min of stirring time with porosity, hardness, and compressive strength values of 0.3250, 108.2738 and 410.6436 respectively. The ANN demonstrated excellent predictive capability, exhibiting correlation coefficients of 0.9617, 0.9536, and 0.9725 for the porosity, hardness, and compressive strength of the composite material, respectively. Next, the ANN was utilized as a fitness function within NSGA-II to perform multi-objective optimization for the porosity, hardness, and compressive strength of the novel material. The resulting Pareto optimal solutions and the optimum production parameters serve as a valuable guide for engineers involved in the design of optimal brake discs and other machine components, utilizing the adv

关键词： multi objective optimization Artificial neural network Genetic algorithm Taguchi grey approach Composite production

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The heating effect analysis of electromagnetic induction heating system based on the multi objective optimization strategy

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JOURNAL OF ENERGY STORAGE 2025年 105卷

作者： Wang, Borui Yan, Mingyin Shenyang Univ Technol Sch Mech Engn Shenyang 110000 Peoples R China

Lithium-ion battery heating in cold weather is necessary to ensure its low-temperature performance and lifetime, so the multi objective optimization heating strategy based on the non-dominated sorting genetic algorithm II is introduced to improve the heating effect of electromagnetic induction heating system, in which the generated Pareto frontier as the research sample is input in K-medoid algorithm in order to reduce the feasible range of solution sets and then get the optimal induction coil parameters combination, and the temperature rise and terminal voltage output performance of battery are characterized by the electrochemical-thermal coupling model. Firstly, under the optimal parameters combination (N = 180, f = 9 kHZ, IA = 6 A), the battery can be heated from 243.15 K to 293.15 K in 1430 s with the temperature difference of 2.74 K, and the temperature field is mainly generated by the heat conduction from active material to mandrel during the heating process due to skin effect and material property. Secondly, the internal resistance is decreased by more than 3 times after the heating, giving rise to the substantial improvement of energy output ability. Finally, neither noticeable discharging capacity retention loss nor apparent voltage output difference is founded after the 200 repeated heating cycles, as compared with the normal charging-discharging cycle at 293.15 K, so the heating strategy has almost no effect on the battery aging and lifetime deterioration. Therefore, the proposed heating strategy can realize a suitable tradeoff among temperature-rising effect, lifespan and low-temperature operating performance, which has substantial potential to be a feasible method for improving the applicability of electric vehicles in a cold weather.

关键词： multi objective optimization Non-dominated sorting genetic algorithm II Electromagnetic induction heating system Electrochemical-thermal coupling model Optimal parameters combination

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Large Language Model Based multi-objective optimization for Integrated Sensing and Communications in UAV Networks

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第4期14卷 979-983页

作者： Li, Haoyun Xiao, Ming Wang, Kezhi Kim, Dong In Debbah, Merouane KTH Royal Inst Technol Div Informat Sci & Engn S-10044 Stockholm Sweden Brunel Univ London Dept Comp Sci London UB8 3PH England Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea Khalifa Univ KU Res Ctr 6G Dept Comp & Informat Engn Abu Dhabi U Arab Emirates Univ Paris Saclay CentraleSupelec F-91192 Gif Sur Yvette France

This letter investigates an un-crewed aerial vehicle (UAV) network with integrated sensing and communication (ISAC) systems, where multiple UAVs simultaneously sense the locations of ground users with radars and provide communication services. To find the trade-off between communication and sensing (C&S) in the system, we formulate a multi-objective optimization problem (MOP) to maximize the total network utility and the localization Cram & eacute;r-Rao bounds (CRB) of ground users, which jointly optimizes the deployment and power control of UAVs. Inspired by the huge potential of large language models (LLM) for prediction and inference, we propose an LLM-enabled decomposition-based multi-objective evolutionary algorithm (LEDMA) for solving the highly non-convex MOP. We first adopt a decomposition-based scheme to decompose the MOP into a series of optimization sub-problems. We second integrate LLMs as black-box search operators with MOP-specifically designed prompt engineering into the framework of MOEA to solve optimization sub-problems simultaneously. Numerical results demonstrate that the proposed LEDMA can find the clear trade-off between C&S and outperforms baseline MOEAs in terms of obtained Pareto fronts and convergence.

关键词： Integrated sensing and communications multi objective optimization large language model large language model

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multi-objective topology optimization of cold plates for enhanced battery thermal management in electric vehicles

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CASE STUDIES IN THERMAL ENGINEERING 2025年 66卷

作者： Wang, Yijun Yu, Minghao Fan, Haiyang Chung, Jae Dong Sejong Univ Dept Mech Engn Seoul 5006 South Korea China Acad Engn Phys Software Ctr High Performance Numer Simulat Beijing 100088 Peoples R China Harbin Engn Univ Yantai Res Inst Yantai 264006 Peoples R China

Efficient thermal management is critical for maintaining the performance, longevity, and safety of lithium-ion batteries, particularly in electric vehicles (EVs). This paper presents a multi-objective topology optimization (TO) approach for designing cold plates in battery thermal management systems (BTMS), with the aim of minimizing both average temperature and power dissipation. The TO model was benchmarked against two established designs: a straight-channel baseline model and the serpentine-channel cold plate used in the GM Volt EV. The optimized TO design exhibited an 82 % reduction in friction factor at Re = 100 and a 75.6 % reduction at Re = 2100, compared to the GM model. Additionally, the TO model's Performance Evaluation Criterion (PEC) was approximately 58 % higher than that of the baseline design and 15 % higher than the GM model at Re = 2100, indicating a superior balance of thermal and hydraulic performance. These results underscore the potential of topology optimization to enhance the design of cold plates for BTMS, offering improved efficiency and effectiveness for high-power applications in EVs.

关键词： multi objective optimization Topology optimization Cold plate GM Volt EV Battery thermal management

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multi-objective optimization for optimal placement of shared battery energy storage systems in urban energy communities

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SUSTAINABLE CITIES AND SOCIETY 2025年 120卷

作者： An, Jongbaek Hong, Taehoon Yonsei Univ Dept Architecture & Architectural Engn Seoul 03722 South Korea Yonsei Univ Dept Architecture & Architectural Engn 50 Yonsei Ro Seoul 03722 South Korea

This study presents a novel multi-objective optimization approach for the optimal placement of shared battery energy storage systems (SBESS) in urban energy communities, balancing economic, technical, and environmental performance. Using real-world data from 191 buildings in Seoul, South Korea, a comprehensive model was developed that incorporates genetic algorithms and pareto optimality to optimize net present value, energy selfsufficiency rates, and peak loads. The results reveal that SBESS placement significantly improves technical and environmental performance compared to scenarios without SBESS, increasing energy self-sufficiency rates by up to 17.44% and reducing peak loads by up to 37.19% across different clusters. However, it currently lacks economic viability, with negative net present values ranging from USD-163,249 to USD-821,469 in all clusters. The optimal placement strategy achieved similar technical and environmental performances as the full placement approach yet required fewer installations. Sensitivity analysis emphasized the importance of electricity market design and policy frameworks in improving the economic feasibility of SBESS. This research contributes to urban energy planning by providing a realistic assessment of SBESS integration, offering valuable insights for policy- makers and planners in balancing sustainability goals with economic constraints in energy community design.

关键词： Battery energy storage system Virtual power plant Photovoltaic Optimal placement Energy community multi objective optimization

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3E multi-objective optimization of Organic Rankine Cycle configurations for a geothermal off-grid system: Power, DHW, and hydrogen production

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ENERGY CONVERSION AND MANAGEMENT-X 2025年 26卷

作者： Mokhtari, A. Jalalvand, M. Imam Ali Univ Dept Flight & Engn Tehran Iran

This study investigates the optimization of an off-grid system for supplying electrical load and domestic hot water (DHW) to a building using geothermal energy. The system generates power through an Organic Rankine Cycle (ORC), with excess energy directed to an electrolyzer for hydrogen production. A residential complex in Tabriz, Iran, with 408 occupants, was selected as the case study, leveraging the availability of geothermal power. Three ORC configurations-basic ORC, ORC with reheater (RHORC), and regenerative ORC (RORC) were optimized in terms of energy, exergy, and economy. The optimization process utilized the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), aiming to maximize energy and exergy efficiency, hydrogen production, and minimize levelized costs of electricity (LCOE) and hydrogen (LCOH), while ensuring that electrical and DHW demands are met. The results demonstrate that the RORC configuration outperforms the other two cycles in all aspects: it produces 44.45 % more power than RHORC and 47.86 % more than ORC, with energy and exergy efficiencies of 49.03 % and 47.01 %, respectively. Furthermore, RORC generates significantly more hydrogen 63.81 % more than RHORC and 69.37 % more than ORC. Additionally, RORC has the lowest LCOE (4.52 cent. kWh- 1) and LCOH (2.69 cent. kWh- 1).

关键词： Geothermal energy Organic Rankine Cycle multi objective optimization Hydrogen production 3E analysis

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multi objective optimization of the baffle parameters for a tubular membrane

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2021年 126卷 14-22页

作者： Fardazad, Amir Mohammad Azadani, Leila N. Amirkabir Univ Technol Dept Chem Engn Tehran Iran

Membrane separation plays an important role in separation processes. In this study, the performance of a tubular membrane filled with sectrozied disk-shaped baffles was studied using three-dimensional computational fluid dynamics (CFD) simulations. Four baffle parameters including the number of sectors in each baffle (N), the angle of each sector (u), the baffle diameter (Db), and the baffle spacing (Lb) were optimized to simultaneously minimize the pressure drop (DP) and maximize the average wall shear stress (t). The central composite design (CCD) of experiment method was used to investigate the effect of the baffle parameters on the pressure drop and average wall shear stress with the minimum number of numerical experiments. Once the designed numerical experiments were performed, the Kriging response surface method was applied to determine the relationships between the pressure drop and the average wall shear stress with the baffle parameters. Finally, the non-dominated sorting genetic algorithm II (NSGA II) was employed to fined the optimum values of the baffle parameters. The baffle diameter had the greatest impact on the pressure drop and average wall shear stress. The optimum baffle parameters were found to be N =6, u = 50 degrees, Db =12 mm, and Lb =10 mm. (c) 2021 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Tubular membrane multi objective optimization Pressure drop Shear stress Permeate flux

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multi objective optimization of reverse osmosis desalination plant with exergy approach

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2016年第10期30卷 4807-4814页

作者： Sadri, Somayyeh Khoshkhoo, Ramin Haghighi Ameri, Mohammad Shahid Beheshti Univ Fac Mech & Energy Engn POB 16765-1719 Tehran Iran

A brackish water reverse osmosis desalination plant based on exergy analysis was simulated and its performance was investigated. The computational model base on diffusion and convection transport mechanisms and concentration polarization concept was developed to predict the performance of RO membrane using different feed water concentration, feed flow rate, feed water pressure, membrane specification and feed water properties. The mathematical model has had good accuracy with reference data. The influence of operating parameters such as feed water pressure and temperature on the performance of the system was studied. Exergetic efficiency and destruction of streams exergy were calculated. Finally, multi-objective optimization for highest exergetic efficiency and permeate flow rate was done.

关键词： Desalination Exergy analysis multi objective optimization Reverse osmosis (RO)

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multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection

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JOURNAL OF GLOBAL optimization 2016年第1期64卷 17-32页

作者： Akhtar, Taimoor Shoemaker, Christine A. Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14853 USA

GOMORS is a parallel response surface-assisted evolutionary algorithm approach to multi-objective optimization that is designed to obtain good non-dominated solutions to black box problems with relatively few objective function evaluations. GOMORS uses Radial Basic Functions to iteratively compute surrogate response surfaces as an approximation of the computationally expensive objective function. A multi objective search utilizing evolution, local search, multi method search and non-dominated sorting is done on the surrogate radial basis function surface because it is inexpensive to compute. A balance between exploration, exploitation and diversification is obtained through a novel procedure that simultaneously selects evaluation points within an algorithm iteration through different metrics including Approximate Hypervolume Improvement, Maximizing minimum domain distance, Maximizing minimum objective space distance, and surrogate-assisted local search, which can be computed in parallel. The results are compared to ParEGO (a kriging surrogate method solving many weighted single objective optimizations) and the widely used NSGA-II. The results indicate that GOMORS outperforms ParEGO and NSGA-II on problems tested. For example, on a groundwater PDE problem, GOMORS outperforms ParEGO with 100, 200 and 400 evaluations for a 6 dimensional problem, a 12 dimensional problem and a 24 dimensional problem. For a fixed number of evaluations, the differences in performance between GOMORS and ParEGO become larger as the number of dimensions increase. As the number of evaluations increase, the differences between GOMORS and ParEGO become smaller. Both surrogate-based methods are much better than NSGA-II for all cases considered.

关键词： Function approximation multi objective optimization Radial basis functions Expensive optimization Global optimization Evolutionary optimization Parallel Metamodel

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multi objective optimization of a tri-reforming process with the maximization of H2 production and minimization of CO2 emission & power loss

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第43期45卷 22480-22491页

作者： Ahmed, Md Zafar Padhiyar, Nitin Indian Inst Technol Gandhinagar Dept Chem Engn Gandhinagar 382355 India

In this study, the process optimization of a tri-reformer reactor is conducted for the synthesis of hydrogen gas from natural gas using multi-objective optimization (MOO) approach. Specifically, four MOO problems are solved using three objective functions, namely maximization of H-2, minimization of CO2, and minimization of power loss. It should be noticed that the power loss is an important economic factor due the large pressure drop and flowrates in packed bed reactors. However, it has not been used as an objective function for the optimization based design and/or operation of fixed bed reactor for reforming process to the best of authors' knowledge. Three of the four MOO problems are 2-objective in nature with all the permutation and combination of the three objectives. The fourth MOO problem is solved considering all the three objectives, simultaneously. For all the MOO problems, feed conditions of O-2, H2O, and Temperature are considered as the optimization variables. The results obtained with 3 objective functions are observed to be superior to the ones obtained from 2 objective problems. (c) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： multi objective optimization Tri-reformer Packed bed reactor Power loss

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