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A genetic algorithm based approach to solve multi-resource multi-objective knapsack problem for vegetable wholesalers in fuzzy environment

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OPERATIONAL RESEARCH 2020年第3期20卷 1321-1352页

作者： Changdar, Chiranjit Pal, Rajat Kumar Mahapatra, Ghanshaym Singha Khan, Abhinandan Univ Calcutta Dept Comp Sci & Engn Kolkata 700106 W Bengal India Natl Inst Technol Dept Math Karaikal 609605 Puducherry India Raja NL Khan Womens Coll Midnapore 721102 W Bengal India

Vegetable wholesaling problem has a vital role in the business system. In this problem, a vegetable wholesaler is supposed to supply raw, fresh vegetables to supermarkets in an efficient way by minimizing time but maximizing profit. In this paper, we have presented a multi-resource multi-objective knapsack problem (MRKP) for vegetable wholesalers. This model is beneficial for a vegetable wholesaler who collects different types of vegetables (objects) from different villages (resources) to a market for selling the vegetables (objects). MRKP is an extension of the classical concept of 0-1 multi-dimensional knapsack problem (KP). In this model, precisely a wholesaler has a limited capacity van/trolley by which he/she collects a set of vegetables from different villages/vegetable fields. In this model, we have assumed that all the vegetables are available for each resource. Each type of vegetable is associated with a weight, a corresponding profit, and a collection time (for a particular resource). The profit, weight, and collection time of objects is different for different resources. Here a time slice is considered for each object to collect it from different villages to a particular destination/market. Also, the profit and time are the two objectives. MRKP aims to find the amount of an object and the corresponding resource name from which it is collected. We have solved the proposed problem in the fuzzy environment. In this paper, we have explained two defuzzification techniques, namely fuzzy expectation and total lambda-integral value method to solve the proposed problem. We have explained a modified multi-objective genetic algorithm (NSGA-II by Deb et al. in IEEE Trans Evol Comput 6(2):192-197, 2002) that is to maximise the profit and minimise the time to collect the objects. We have considered a multi-objective benchmark test function to show the effectiveness of the proposed genetic algorithm. Modification is made by introducing refinement operation. An extensive

关键词： Fuzzy number multi-resource knapsack problem multi-objective genetic algorithm Refinement Vegetable wholesaling

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multi-parameter optimization of multiple vapor-cooled shields integrated with para-to-ortho catalytic conversion based on self-pressurization model of liquid hydrogen tank and MOGA

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APPLIED THERMAL ENGINEERING 2025年 263卷

作者： Li, Ke Wen, Jian Xin, Biping Zhu, Shun Xi An Jiao Tong Univ Sch Energy & Power Engn Dept Refrigerat & Cryogen Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China

The temperature difference between the inside and the outside of liquid hydrogen storage tank is very large, so VMLI (vacuum multi-layer insulation) coupled with VCS (vapor cooled-shield) is generally introduced to shield external heat leakage. Further, VCS is filled with catalyst for hydrogen para-to-ortho catalytic conversion (POC), which can release cold energy to shield the external heat leakage. Based on the self-pressurization model of tank constructed in the research, it is found that the mass flowrate in VCS m (center dot) vcs , the opening moment of VCS t open , the duration time of VCS t d , and VCS DP(dimensionless position) have obvious effect on the total mass of gas hydrogen consumed by VCS and the dormancy of tank, which are respectively described by two dimensionless factors 7c and 7d. It is hoped that the tank dormancy can be extended with as little hydrogen consumed by VCS as possible, by choosing appropriate (m(center dot)vcs, t open , t d , VCS DP). Especially for TVCS, six parameters need to be determined, including (m(center dot)vcs, t open , t d ) and DP of the three VCSs. Therefore MOGA (multi-objective genetic algorithm) is innovatively introduced to help search for the optimized (m(center dot)vcs, t open , t d , VCS DP) in high-dimensional input parameter space, which can make 7c and 7d Pareto-optimal, and the corresponding (7c, 7d) form the Pareto front. The Pareto front can be fitted as a straight line through the origin. The slope parameter of the line determines the theoretical limit of 7d when 7c is fixed. It means that, when the total mass of gas hydrogen consumed by VCS is fixed, the dormancy has a theoretical limit. When the TVCS-POC (triple vapor-cooled shield integrated with hydrogen para-to-ortho catalytic conversion) is adopted, (7c, 7d) calculated according to the optimized (m(center dot)vcs, t open , t d , VCS DP)(the three optimized points in high-dimensional input parameter space) are compared with that calculated according

关键词： Liquid hydrogen tank Self-pressurization Triple vapor-cooled shield Dormancy multi-objective genetic algorithm Pareto-optimal

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multi-objective genetic algorithms for Trajectory Optimization of Space Manipulator

Multi-Objective Genetic Algorithms for Trajectory Optimizati...

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4th IEEE Conference on Industrial Electronics and Applications

作者： Liu, Zhengxiong Huang, Panfeng Yan, Jie Liu, Gang Northwestern Polytech Univ Res Ctr Intelligent Robot Coll Astronaut Xian 710072 Peoples R China Infineon Technol Xian Ltd Xian Peoples R China

ISBN: (纸本)9781424427994

This paper propose a multi-objective optimization algorithm to optimize the motion path of space manipulator with multi-objective function. In this formulation, multi-objective genetic algorithm (MOGA) is used to minimize the multi-objective function. The planning procedure is performed in joint space and with respect to all constraints, such as joint angle constraints, joint velocity constraints, torque constraints. We use a MOGA to search the optimal joint inter-knot parameters in order to realize the optimal motion trajectory for space manipulator. These joint inter-knot parameters mainly include joint angle and joint angular velocities. The simulation results test that the proposed multi-objective genetic algorithm has satisfactory performance.

关键词： Space manipulator multi-objective genetic algorithm optimization

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Layout optimization for offshore wind farms considering both fatigue damage and power generation

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RENEWABLE ENERGY 2025年 246卷

作者： Peng, Wangxuan Li, Baoliang Ge, Mingwei Li, Xintao Ding, Wei Li, Bo North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China China Longyuan Power Grp Co Ltd Beijing 100034 Peoples R China

Fatigue damage is a crucial factor affecting the lifespan and reliability of wind turbines. However, this factor is seldom considered in the phase of wind farm layout optimization (WFLO). This oversight can lead to increased operating and maintenance costs, especially for offshore wind farms. To fill this technical gap, a multi-objective WFLO framework considering both fatigue damage and power generation is proposed. Via analytical wake models of wind speed and turbulence, the fatigue damage of each turbine can be rapidly evaluated accounting for both power production and wake turbulence. Typically, the Horns Rev wind farm is taken as a benchmark, and two indicators of fatigue damage, the maximum (Fmax) and mean (Fmean) fatigue damage of all turbines, are considered in WFLO, respectively. The optimized layouts can achieve an increase in total power generation of over 2 % compared to the original layout, while reducing fatigue damage by about 1 %. Moreover, compared to single-objective WFLO focused solely on power generation, the multi-objective approach achieves a reduction of about 3.2 % in Fmax, while maintaining a comparable power output level. Further investigation shows that the indicator of Fmax in WFLO performs better to effectively reduce and balance the fatigue damage of all turbines.

关键词： Wind farm layout optimization multi-objective genetic algorithm Power generation Turbine fatigue damage

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A hybrid sonic black hole for enhancing low-frequency sound absorption

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PHYSICA SCRIPTA 2025年第5期100卷 055003-055003页

作者： Xian, Yuxuan Cheng, Baozhu Liu, Yang Zhang, Yanni Zhang, Zhiqiang Rugwizangoga, Blaise Kevin Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212003 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Lab Ocean Acoust & Sensing Xian 710072 Peoples R China Soochow Univ Sch Rail Transportat Suzhou 215131 Peoples R China Suzhou Dongling Vibrat Test Instrument Co Ltd Suzhou 215163 Peoples R China Nanjing Univ Sci & Technol Inst Launch Dynam Nanjing Peoples R China Natl Key Lab Complex Multibody Syst Dynam Beijing Peoples R China

Sonic black hole (SBH) is a kind of broadband sound absorber, which features lightweight and high-efficiency. However, achieving low frequency sound absorption at a deep-subwavelength thickness remains a challenge for conventional SBHs. Herein, a hybrid sonic black hole (HSBH) with gradually varying neck-embedded Helmholtz resonators (HRs) and micro-perforated panels (MPPs) is proposed. The working bands are broaden at low frequencies with sub-wavelength and broadband sound absorption in the mid-and-high frequency range. Such HSBH exhibits excellent potential in broadband sound absorption with sub-wavelength. The transfer matrix method (TMM) is used to analyze the low-frequency sound absorption of HSBH, which is validated by the finite element method (FEM). It is shown that the proposed HSBH exhibits high sound absorption with sub-wavelength (the frequency of first peak is 102 Hz, about lambda/30 ). The enhanced low-frequency sound absorption of the proposed HSBH is also validated by the experiment results. Meanwhile, the influences of various structural parameters on the low-frequency properties and broadband characteristics of HSBHs are also revealed. A fast and elitist nondominated sorting genetic algorithm (NSGA-II) is introduced to optimize the HSBH. Finally, a coplanar HSBHs are also designed to achieve a broadband sound absorption from 100 to 3000 Hz.

关键词： sonic black holes sub-wavelength sound absorber acoustic metamaterials multi-objective genetic algorithm coplanar HSBHs

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The robotic assembly line balancing problem under task time uncertainty

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第5-6期137卷 2991-3011页

作者： Zacharia, Paraskevi Nearchou, Andreas C. Univ West Attica Dept Ind Design & Prod Engn Athens 12244 Greece Univ Patras Dept Business Adm Patras Achaia Greece

Consideration is given to the robotic assembly line balancing problem (RALBP) under uncertain task (operation) times, a critical challenge encountered in automated manufacturing systems.. RALBP is a decision problem which seeks the optimal assignment of the assembly work as well as the most suitable robots to the workstations of the assembly line with respect to objectives related to the capacity of the line or/and its cost of operation. When multiple types of robots with different capabilities are being used, task times may vary depending on robot type and the nature of the task. Task variation is expected to be small for simple tasks but may be quite large for complex and failure sensitive operations. To deal with uncertainty in task variation we used fuzzy logic theory. First, we introduce formally the fuzzy RALBP and then we describe deeply the fuzzy representation of the task times. We address RALBP with respect to two optimization objectives namely, the production rate and workload smoothing. Since the problem is known to be NP-hard, we explore its heuristic solution through a new robust multi-objective genetic algorithm (MOGA) aiming to determine the Pareto optimal set. Simulation experiments assess MOGA's efficiency in comparison to the famous NSGA-II and MOPSO algorithms, while also exploring the trade-off between the two conflicting objectives.

关键词： Robots Flow lines Fuzzy task times multi-objective genetic algorithm Pareto optimization Metaheuristics

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An innovative optimal integrated solar-lignocellulosic biomass polygeneration system with biorefinery and solid oxide electrolyzer cell

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ENERGY CONVERSION AND MANAGEMENT 2025年 327卷

作者： Morid, Mehdi Manesh, Mohammad Hasan Khoshgoftar Univ Qom Energy Environm & Biol Syst Res Lab EEBRlab Div Thermal Sci & Energy Syst Dept Mech EngnFac Technol & Engn Qom Iran

Polygeneration systems combine various energy sources and processes to produce multiple products for residential and industrial needs in a unified system, offering enhanced sustainability and resilience compared to single-process systems. Optimizing these systems maximizes energy resource utilization. This study introduces a comprehensive polygeneration system generating eight products to satisfy power, cooling, heating, and secondary product requirements like fresh water, hydrogen, oxygen, carbon dioxide, and ethanol. The system is based on two renewable energy sources, solar and biomass, as well as traditional methane energy. Through a comprehensive evaluation encompassing energy, exergy, exergo-economic, exergo-environmental, emergoeconomic, and emergo-environmental considerations, the system is thoroughly evaluated. Mathematical modeling in MATLAB assesses thermal, economic, and ecological factors for optimization. Validation is performed using Thermoflex and Aspen Plus for an ethanol and power co-production biorefinery from corn stover. multi-objective optimization employs a machine learning and genetic algorithm approach in two phases. The initial phase targets three objectives for the hybrid multi-effect distillation adsorption desalination unit to optimize fresh water production, performance ratio, and energy consumption. Our results demonstrate that in the optimal scenario, there is an 8.2% increase in fresh water production rate, a 10.2% increase in performance ratio, and a 9.2% reduction in specific energy consumption. In the second phase, the overall system is optimized with the objective of maximizing thermodynamic performance while minimizing economic and environmental indicators. The six-objective optimization results in a 2.54% increase in overall energy efficiency, a 3.38% increase in overall exergy efficiency, and a 59.66% improvement in the coefficient of performance of the ejector refrigeration cycle. Additionally, it leads to a 1.88% reduction i

关键词： multi-objective genetic algorithm Solid Oxide Electrolysis Cell Recurrent Neural Network Optimization Bioethanol Biorefinery Renewable energy

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An Improved multi-objective Adaptive Niche genetic algorithm Based On Pareto Front

An Improved Multi-Objective Adaptive Niche Genetic Algorithm...

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IEEE International Advance Computing Conference

作者： Zhang, Jingjun Shang, Yanmin Gao, Ruizhen Dong, Yuzhen Hebei Univ Engn Dept Sci Res Handan Peoples R China

ISBN: (纸本)9781424429271

For multi-objective optimization problems, an improved multi-objective adaptive niche genetic algorithm based on Pareto Front is proposed in this paper. In this algorithm, the rank value and the niche value are introduced to evaluate the individuals. The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according to solution quality. The experimental results show that this algorithm convergent faster and is able to achieve a broader distribution of the Pareto optimal solution.

关键词： Pareto solution Pareto dominate multi-objective genetic algorithm niche genetic algorithm adaptive

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Investigation of high-compactness and high-efficiency TPMS precoolers for precooled aero-engine

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ENERGY 2025年 319卷

作者： Huang, W. S. Ning, H. Y. Tang, G. H. Xi An Jiao Tong Univ Sch Future Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Key Lab Thermo Fluid Sci & Engn MOE Xian 710049 Peoples R China

The precooler is the core functional component of the precooled air-breathing engines, which requires high power-to-weight ratio and robustness. However, the conventional compact tubular precooler suffers from low reliability, poor manufacturability and uneven flow distribution. The present study proposes a novel precooler based on triply periodic minimal surface (TPMS) in polar coordinate form. Firstly, the linear approximation method is introduced and regression models are established by response surface methodology (RSM) to determine the geometry parameters of polar TPMS. Then, the accuracy of the numerical method for TPMS is validated by experimental tests and new correlations for thermal and hydraulic performances of TPMS precooler are fitted. Subsequently, the segmental thermal design method and multi-objective genetic algorithm are combined for the multi-objective optimization of the precooler. Finally, the superior optimization objective combination selected by the decision-making method is compared with the conventional compact tubular precooler. The results indicate that the optimized TPMS precooler volume is reduced by 27.8%, with 23.8% increase in power-to- weight ratio. Additionally, the TPMS precooler fabricated by additive manufacturing can exhibit high reliability. The present methods for the novel TPMS precooler are also straightforward for high efficient and high compact heat exchangers in other fields under harsh thermal environment.

关键词： Triply periodic minimal surface Linear approximation method Segmental design method Parameter analysis Response surface method multi-objective genetic algorithm

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A High-Reliability Small-Area Task Offloading Mechanism With Trust Evaluation and Fuzzy Logic in Power IoTs

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 2935-2948页

作者： Wang, Suhong Qin, Tuanfa Chen, Tingting Guo, Wenhao Hu, Yongle Sun, Hongmin Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China Guangxi Vocat Tech Inst Ind Nanning 530001 Peoples R China Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China Guangxi Univ Sch Elect Engn Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China Runjian Co Ltd Nanning 530007 Peoples R China Guangxi Vocat Tech Inst Ind Nanning 530001 Peoples R China

In order to solve the problem that high-priority tasks can not be processed timely and reliably due to the disorder of multi-task and dynamicity in Power Internet of Things(PIoTs), a high-reliability small-area task offloading mechanism with trust evaluation and fuzzy logic(HRSATF) is proposed. First, considering task priority, preemptive priority queue is introduced to ensure high-priority tasks processed preferentially, and minimum resource allocation coefficients(MRACs) of tasks are solved to ensure the effectiveness of offloading. Second, the trust model between smart device(SD) and edge server(ES) is established, and ESs are divided into three priorities based on trust value and computing power by fast non-dominated sorting. Thirdly, fuzzy logic is applied to select target ES when the priorities of task and ES do not match or the ES is offline, and MRAC is used to schedule tasks between SD and ES. Finally, NSGA2 is modified (MNSGA2) to verify the effectiveness of HRSATF in terms of success rate, time, power consumption and load balancing, where success rate is increased by 102.3%, and time and power consumption are decreased by 90.7%, 89.3% at most, respectively.

关键词： Fuzzy logic Costs Reliability Blockchains Resource management Mobile computing Computational modeling Cloud computing Smart grids Delays multi-objective genetic algorithm preemptive priority resource allocation task offloading trust evaluation

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