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Multi-objective optimization on combined heat pump and mechanical vapor recompression system for waste heat recovery of offshore wind power converter

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DESALINATION 2025年 601卷

作者： Hu, Xiaowei Shi, Chenyang Dong, Shengming Fu, Xingyu Ma, Tianya Chen, Qijing Tianjin Univ Commerce Sch Mech Engn Tianjin 300134 Peoples R China

Offshore wind power converter station generates a significant amount of low-temperature waste heat, requiring tremendous freshwater for discharge. Conventional waste heat utilization solutions are no longer effective because their products are unable to be reused in situ. Therefore, two types of seawater desalination systems combining heat pump and mechanical vapor recompression were proposed and investigated by multi-objective optimization and sensitivity analysis. From a view of thermodynamics and economics, the total heat transfer area and total power consumption were adopted as objective functions. An improved non-dominated sorting genetic algorithm was adopted to determine the key parameters, including the condensation temperature of the heat pump, the temperature of seawater after the electronic expansion valve, and the condensation temperature of water vapor in the seawater and water vapor heat exchanger. The Pareto front and approximate ideal solution sorting methods determined the optimal operational states of the two systems as [70.5, 20, 28.57] and [61.42, 40, 73.48], respectively. From the sensitivity analysis, the condensation temperature of the condenser is the most critical parameter. Moreover, HP-MVR-1 and HP-MVR-2 need 139.75 kW and 158.17 kW electric power if freshwater is fully utilized in situ. It may be an effective solution for waste heat in offshore wind power converters.

关键词： Mechanical vapor recompression Heat pump Seawater desalination Multi-objective optimization non-dominated sorting genetic algorithm Offshore power wind converter

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Multi-objective optimization of Tesla valve channel battery cold plate with nanofluid by RSM and NSGA-II

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JOURNAL OF ENERGY STORAGE 2025年 115卷

作者： Ren, Fan Li, Qibin Wang, Penglai Chongqing Univ Sch Energy & Power Engn Key Lab Low grade Energy Utilizat Technol & Syst Minist Educ Chongqing 400044 Peoples R China Natl Univ Singapore Dept Mech Engn 9 Engn Dr 1 Singapore 117575 Singapore

Tesla valve cold plate is a promising technology in battery thermal management system, but its temperature is uneven, the pressure loss is large, and the heat transfer effect needs to be further improved. Therefore, computational fluid dynamics simulation is employed to analysis the three-dimensional Tesla valve cold plate with different flow conditions. The effects of flow direction combinations of Tesla valve pipes, discharge powers, types of nanofluids, volume fractions of nanofluids, initial temperature and initial velocity of nanofluids on the fluid heat transfer characteristics of cold plate are discussed. The results show that the cooling effect of cold plate is the best in the way of outer tube downstream and inner tube countercurrent. At the same time, the Nusselt number and average heat flux increased by 8.68 % and 2.73 % respectively compared with pure downstream. By comparing different kinds of nanofluids, it is found that the 5 % Cu nanoparticles and water mixed nanofluid has the best heat transfer characteristics. Finally, the response surface method (RSM) and non-dominated genetic algorithm (NSGA-II) are used to optimize the working conditions of nanofluids. The optimal comprehensive heat transfer effect can be obtained when the initial velocity, temperature and volume fraction are 0.13 m/s, 288 K and 4.38 %, respectively.

关键词： Nanofluids Tesla valve Battery cold plate non-dominated sorting genetic algorithm Response surface method

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A parallel genetic algorithm for multi-criteria path routing on complex real-world road networks

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APPLIED SOFT COMPUTING 2025年 170卷

作者： Sharma, Harish Galvan, Edgar Mooney, Peter Natl Univ Ireland Maynooth Dept Comp Sci Nat Inspired Computat Res Grp Maynooth Ireland Natl Univ Ireland Maynooth Hamilton Inst Maynooth Ireland Natl Univ Ireland Maynooth Dept Comp Sci Nat Inspired Computat Res Grp Lero Maynooth Ireland

A Parallel genetic algorithm (PGA) specifically designed for multi-criteria vehicle routing is described in this work. The algorithm aims to enhance the existing routing methods by offering users the ability to choose their preferred path from a set of optimal paths optimised on multiple objectives. The objectives are optimised using a novel fitness metric that prioritises minimising path length while also maximising access to specific amenities such as pubs, hotels, and charging stations. The developed approach, called Parallel Optimal-route Search (POS), follows a hybrid model using both global parallelisation and island-based approaches. A Loop-Free Path-Composer (LFPC) is described and this genetic operator generates new paths for evaluation and is shown to yield a more diverse set of solutions in contrast to other commonly used approaches, such as Node Based Crossover and Path Mutation (NBCPM). Our approach is validated on highly complex, large-scale real-world road networks, with sizes ranging from 3,000 and 10,000 nodes. We present a systematic study comparing the performance of our proposed LFPC operator against the traditional NBCPM operators. Additionally, we evaluate the effectiveness of our proposed POS algorithm in comparison to the well-known non-dominated sorting genetic algorithm II and III.

关键词： Vehicle routing problem (VRP) Parallel genetic algorithm (PGA) Hybrid model Improved crowding distance (ICD) non-dominated sorting genetic algorithm (NSGA) II and III Pareto-optimal solutions

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A Multi-Objective Sensor Placement Method Considering Modal Identification Uncertainty and Damage Detection Sensitivity

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BUILDINGS 2025年第5期15卷

作者： Pei, Xue-Yang Hou, Yuan Huang, Hai-Bin Zheng, Jun-Xing Yancheng Inst Technol Sch Civil Engn Yancheng 224051 Peoples R China Hebei Univ Technol Sch Civil & Transportat Engn Tianjin 300401 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

Structural Health Monitoring relies on accurate modal identification and effective damage detection to assess structural performance and safety. However, traditional sensor placement methods struggle to balance modal identification uncertainty, which arises from limited sensor coverage and measurement noise and damage detection sensitivity, which requires sensors to be optimally positioned to capture structural stiffness variations. To address this challenge, this study proposes a multi-objective sensor placement optimization method based on the non-dominated sorting genetic algorithm. The method introduces two key objective functions: minimizing modal identification uncertainty by leveraging Bayesian modal identification theory and information entropy and maximizing damage detection sensitivity by incorporating an entropy-based measure to quantify the uncertainty in stiffness variation estimation. By formulating the problem as Pareto-based multi-objective optimization, the method efficiently explores a trade-off between the two competing objectives and provides a diverse set of optimal sensor placement solutions. The proposed approach is validated through numerical experiments on a simply supported beam and a benchmark bridge structure, demonstrating that different optimization objectives lead to distinct sensor placement patterns. The results show that solutions prioritizing modal identification distribute sensors across the structure to improve global response estimation, while solutions favoring damage detection concentrate sensors in critical areas to enhance sensitivity. The proposed method significantly improves sensor placement strategies by offering a systematic and flexible framework for SHM applications, enabling engineers to tailor monitoring strategies based on specific structural assessment needs.

关键词： multi-objective sensor placement modal identification uncertainty damage detection sensitivity Pareto optimization non-dominated sorting genetic algorithm

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Multi-Objective optimization of a cascaded supercritical CO2 brayton cycle with ejector-enhanced transcritical CO2 and flash tank absorption refrigeration cycles

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ENERGY CONVERSION AND MANAGEMENT-X 2025年 26卷

作者： Khan, Tausif Elahi Sakib, Sakibul Haque Sakib, Nazmus Hossain, Tanvir Ehsan, M. Monjurul Khan, Yasin Islamic Univ Technol IUT Dept Mech & Prod Engn MPE Gazipur 1704 Bangladesh

The research introduces a novel system that combines power generation and cooling system by integrating an ejector-assisted transcritical CO2 refrigeration (ETCR) subsystem and a flash tank installed compression absorption refrigeration (FCAR) subsystem, cascaded with a supercritical CO2 recompression Brayton cycle. The FCAR subsystem contributes additional cooling capacity, while the ETCR subsystem enhances the chiller load by minimizing waste heat from the cooler. These added loads significantly enhance the system's overall power output, leading to higher thermal and exergy efficiencies. The thermal performance of the integrated system is evaluated through extensive parametric analyses implemented in Python, considering variables such as turbine and compressor inlet temperatures, minimum pressure, pressure ratio, evaporator temperature, and the generator's hot and cold-end pinch temperatures. Multi-Objective Optimization (MOO) is used to identify the ideal operational parameters for maximizing thermal and exergy efficiencies. For MOO and Multi-Criteria Decision Making (MCDM), the non-dominated sorting genetic algorithm II (NSGA-II) and TOPSIS methods are used, respectively. The optimization results indicate that the system achieves peak performance when the compressor entry temperature is set at 33.03 degrees C, and the pressure ratio is 3.52. Under these optimal conditions, the system attains thermal and exergy efficiencies of 61.8 % and 65.76 %, respectively. With a 100 MW input power, the system delivers a net output of 40.52 MW, along with a chiller load of 10.96 MW and a cooling load of 10.32 MW, leading to 16.14 % greater thermal efficiency and 4.4 % improved exergy efficiency over a conventional system. Additionally, exergy analysis identified a total exergy destruction of 26 MW, with significant losses observed in the reactor and R41 evaporator (10.24 % and 5.48 %, respectively). Under optimal conditions, the system can produce 40.52 MW of total work, a 10

关键词： Ejector refrigeration Multi-Objective Optimization Multi-Criteria Decision Making non-dominated sorting genetic algorithm Exergy destruction Supercritical CO2

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Optimization and performance prediction of phosphate tailing-based geopolymer formulations using D-optimal mixture design and multi-algorithm collaboration

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 199卷

作者： Jiang, Changbao Yang, Yihao Wang, Menglai Xu, Wenfeng Guo, Yongjie Li, Shujian Xu, Shuai Liao, Junsen Yunnan Phosphate Chem Grp Co Ltd Kunming 650600 Peoples R China Chongqing Univ Sch Resources & Safety Engn State Key Lab Coal Mine Disaster Dynam & Control Chongqing 400030 Peoples R China

To promote the resource utilization of large-scale industrial solid wastes such as phosphate tailings, geopolymers were prepared using phosphate tailings, metakaolin, silica fume, and alkali activators as raw materials. In this study, the D-optimal mixture design method was employed as the experimental framework. Based on the experimental data, statistically significant Scheffe polynomial models were developed to analyze the relationships and evolution patterns between material proportions and strength responses. Building on the Scheffe polynomial models, a hybrid prediction model was developed by integrating an improved particle swarm optimization (PSO) algorithm with the non-dominated sorting genetic algorithm II (NSGA-II) to predict the compressive and flexural strengths of the geopolymer. The predictive performance of the hybrid model was then compared with that of the original Scheffe polynomials. The results indicate that the traditional Scheffe polynomial model yields a prediction error of 5.47 % for compressive strength and 31.82 % for flexural strength. In contrast, the integration of PSO and NSGA-II significantly enhances both prediction accuracy and multi-objective optimization performance, reducing the prediction errors for compressive and flexural strengths to 3.64 % and 11.24 %, respectively. This approach enables dual-objective optimization and accurate prediction of the physical and mechanical properties of phosphate tailings-based geopolymers. By adjusting the mix proportions, the material properties can be tailored to suit various engineering applications, providing an advanced approach to promote the recycling and utilization of industrial solid wastes such as phosphate tailings.

关键词： D-optimal mixture design Phosphate tailings Particle swarm optimization non-dominated sorting genetic algorithm

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Multi-objective mixed mode sugarcane transportation model using fuzzy NSGA

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OPSEARCH 2025年第1期62卷 149-177页

作者： Kurade, Sandesh Latpate, Raosaheb Gedam, Vinayak MES Abasaheb Garware Coll Autonomous Dept Stat Karve Rd Pune 411004 India Savitribai Phule Pune Univ Dept Stat Pune 411007 India

The sugar factory business plays a significant role as a source of employment for the developed countries like India, Brazil, South Africa etc. Sugar factory business in India impacts the livelihood of almost each and every sugarcane farmers and workers employed in these factories. Hence in this paper, a multi-objective mixed mode transportation model for sugarcane industry is formulated. To make the model realistic, uncertainty in costs, time, supply and demand are considered. Uncertainty in these key parameters of the sugarcane industry are occur due to competitive nature of the sugar market. The sugar industries located to nearby places to the market always have huge impact on the sugar market. In the formulated model, parameters are expressed by means of triangular fuzzy number due to uncertainty in the market. Various modes of transportation with their restricted capacities like bullock carts, trucks, tractors, tractor trolleys etc. are considered in the developed model. For solving the model, a new optimization algorithm using fuzzy set theory, convex set theory and evolutionary algorithm is developed. For applicability of the model, a real world case study of supply chain of the co-operative sugarcane factory located in Maharashtra state for season 2018 is considered. Finally, Pareto decision space of the model is discussed at different levels of uncertainty. The developed model has several industrial applications. It will be helpful to sugar factories to optimize their supply chains.

关键词： Mixed mode Multi-objective non-dominated sorting genetic algorithm Supply chain Transportation Triangular fuzzy number

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Multi-Objective Optimization Design of PCS Box Girder Bridges with Small and Medium Spans Using genetic algorithms

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BUILDINGS 2025年第3期15卷

作者： Li, Zhijie Qi, Jianan Wang, Jingquan Southeast Univ Sch Civil Engn Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 211189 Peoples R China Southeast Univ Natl Key Lab Safety Durabil & Hlth Operat Long Spa Nanjing 211189 Peoples R China Southeast Univ Bridge Engn Res Ctr Southeast Univ Nanjing 211189 Peoples R China Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China

With the development of algorithms for autonomous decision-making in the field of structural engineering, the design of precast concrete segment (PCS) box girder bridges faces new challenges. This paper proposes using a multi-objective optimization method based on genetic algorithms for the rapid design of PCS box girder bridges with small and medium spans. By considering 20 design parameters such as the physical dimensions of the box girder cross-section, material properties, and prestressing parameters, the paper formulates and quantifies three objective functions: cost, safety, and structural performance. The multi-objective optimization was conducted using four optimization algorithms (NSGA-II, NSGA-III, GDE3, and PSO). An optimization evaluation index (phi[F(x)]) was established and weights were assigned to different optimization objectives. A specific design case based on the general diagram of a 3 x 25 m-long continuous PCS box girder bridge was carried out. The results indicate that genetic algorithms performed exceptionally well on this problem, with the NSGA-III algorithm achieving the best phi[F(x)] value of 0.2789 among all algorithms. A performance analysis was conducted on various optimization models using box plots and sensitivity studies. Scatter plots and surface plots of the Pareto front of the optimized solutions were generated, and corresponding cross-sectional design drawings were created based on the two proposed solutions. Compared with the general graph, the design cases provided by the NSGA-III algorithm model have a change rate of 8.03%, -0.29%, and 75.49% in the three optimization objectives, respectively, indicating a significant improvement effect. The research content of this paper provides a reasonable direction for future studies on intelligent bridge design methodologies.

关键词： multi-objective optimization precast concrete segmental bridge box girder non-dominated sorting genetic algorithm

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Optimization of grid-connected solar PV systems with Hybrid Energy Storage System: A case study of the Sri Lankan power system

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JOURNAL OF ENERGY STORAGE 2025年 114卷

作者： Ganege, H. C. Chandima, D. P. Karunadasa, J. P. Wheeler, Patrick Univ Moratuwa Fac Engn Dept Elect Engn Moratuwa 10400 Sri Lanka Univ Ruhuna Fac Technol Dept Engn Technol Matara 81000 Sri Lanka Univ Nottingham Dept Elect & Elect Engn Univ Pk Nottingham NG7 2RD England

Greenhouse gas emissions from fossil fuel-based electricity generation significantly contribute to climate change. This research aims to mitigate these emissions by reducing reliance on fossil fuels and maximizing photovoltaic (PV) energy generation. It is supported by a grid-connected Hybrid Energy Storage System (HESS) integrating lithium-ion Battery Storage (BS) and Pumped Hydro Storage (PHS). An optimization algorithm is employed to minimize total expenditures, including capital, operational, replacement costs, as well costs associated with solar curtailment and supplemental thermal energy over the lifetime of the system. The non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem through the open-source Python framework Pymoo (multi-objective optimization in Python). Optimal capacities for solar PV and BS are determined for short-term intervals, while PHS capacities are evaluated based on regional geological feasibility. A novel energy management strategy ensures an effective balance between energy generation and storage. Optimization results are visualized through Pareto optimal charts supplemented by economic and environmental analyses to identify sustainable scenarios. Sensitivity analysis further refines the optimal capacities for solar PV, BS, and PHS. The proposed methodology is validated the Sri Lankan power system. A detailed roadmap is developed to guide the incremental additions of solar BS, and PHS capacities. Final results indicate minimal total cost variations, ranging from -14.05% to 56.69%, and an increase in solar PV generation between 2.19% and 5.13%, due to fluctuations in the interest rate the country.

关键词： non-dominated sorting genetic algorithm Multi-objective optimization in Python Hybrid Energy Storage Maximize solar Solar curtailment

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Multi-sensor system deployment planning method for underwater surveillance based on formation characteristics

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AD HOC NETWORKS 2025年 170卷

作者： Yan, Zheping Cai, Sijia Hou, Shuping Zhang, Mingyao Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Ctr Qingdao 266000 Peoples R China Harbin Engn Univ Coll Mech & Elect Engn Harbin 150001 Peoples R China

The deployment planning issue for a multi-sensor system comprising a limited number of sensors designed to detect underwater intrusion targets is defined as a multi-objective NP-hard problem. This problem is constituted by two competing and incommensurable optimization objectives: "larger sensor coverage" and "higher probability of detecting intrusion targets". The map of the mission area is transformed into a topological map through the application of polygon fitting and segmentation based on Delaunay triangulation. This study employs a characteristics-based non-dominated sorting genetic algorithm (CBNSGA) to address the deployment planning issue of the multi-sensor system. In this algorithm, Mean-Shift clustering is employed to yield characteristics information through the clustering of the multi-sensor system formation. Subsequently, this information is employed to enhance the crossover, mutation, and selection strategies. Adaptive parameters are designed to accelerate convergence and avoid local optima. Additionally, the Cauchy inverse cumulative distribution operator is employed to enhance the mutation step. The feasibility and effectiveness of the CBNSGA in multi-sensor system deployment planning are demonstrated through simulation and comparison with other algorithms.

关键词： Multi-sensor system Multi-objective optimization Underwater surveillance non-dominated sorting genetic algorithm Mean-Shift clustering Barrier coverage

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