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PMS-Sorting:A New Sorting Algorithm Based on Similarity 认领 引用
1
作者 Hongbin Wang Lianke Zhou +4 位作者 Guodong Zhao Nianbin Wang Jianguo Sun Yue Zheng Lei Chen 《Computers, Materials & Continua》 SCIE EI 2019年第4期229-237,共9页
Borda sorting algorithm is a kind of improvement algorithm based on weighted position sorting algorithm,it is mainly suitable for the high duplication of search results,for the independent search results,the effect is... Borda sorting algorithm is a kind of improvement algorithm based on weighted position sorting algorithm,it is mainly suitable for the high duplication of search results,for the independent search results,the effect is not very good and the computing method of relative score in Borda sorting algorithm is according to the rule of the linear regressive,but position relationship cannot fully represent the correlation changes.aimed at this drawback,the new sorting algorithm is proposed in this paper,named PMS-Sorting algorithm,firstly the position score of the returned results is standardized processing,and the similarity retrieval word string with the query results is combined into the algorithm,the similarity calculation method is also improved,through the experiment,the improved algorithm is superior to traditional sorting algorithm. 展开更多
关键词 Meta search engine result sorting query similarity Borda sorting algorithm position relationship
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An Only-Once-Sorting Algorithm 认领 引用
2
作者 Xu Xusong Zhou Jianqin Guo Feng 《Wuhan University Journal of Natural Sciences》 CAS 1996年第1期38-41,共4页
This paper provides a new sorting algorithm called 'Only-Once-Sorting' algorithm a mathemati cal formula,this algorithm can put elements in the positions they should be stored only once,then compacts them.The ... This paper provides a new sorting algorithm called 'Only-Once-Sorting' algorithm a mathemati cal formula,this algorithm can put elements in the positions they should be stored only once,then compacts them.The algorithm completes sorting a sequence of n elements in a calculation time of O(n ). 展开更多
关键词 mathematical formula onlv-once-sorting sorting algorithm
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Improvement of Counting Sorting Algorithm 认领 引用
3
作者 Chenglong Song Haiming Li 《Journal of Computer and Communications》 2023年第10期12-22,共11页
By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting ... By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data. 展开更多
关键词 Sort Algorithm Counting Sorting Algorithms Complexity Internal Features
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Techno-economic co-optimization of CO2enhanced oil recovery strategies in a tight oil reservoir using coupled improved evolutionary algorithm and machine learning framework 认领 引用
4
作者 Shu-Qin Wen Bing Wei +2 位作者 Jun-Yu You Nan-Jiang Leng William Ampomah 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2639-2654,共16页
Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exc... Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exchange ratio,CO2storage,and net present value remainschallenging.This study develops an integrated machine learning(ML)-based framework for multi-objective optimization of CO2-EOR.A high-resolution reservoir simulation was constructed from field data,and Latin hypercube sampling generateddiverse scenarios for proxy training.Mantel's test quantified correlations between input parameters and performance metrics,showing that injection strategy strongly controls net present value,whereas geological properties dominate CO2storage.Three ML models—random forest(RF),support vector regression,and artificial neural networks—were evaluated,with RF selected for its superior performance on small datasets.RF was embedded into an improved non-dominatedsorting genetic algorithm II,enhanced with grey difference degree,crowding distance,and adaptive differential evolution to improve diversity and efficiency.Finally,the technique for order preference by similarity to ideal solution ranked Pareto-optimal solutions through integrating oil productivity,storage,and economics.The proposed framework operationalizes simultaneoushigh-efficiency tight oil recovery and field-scale CO2geological storage,delivering quantitative design rules that embed low-carbon practice into upstream operations and advance the energy sector's greenerand sustainable transition. 展开更多
关键词 CO2enhanced oil recovery Multi-objective optimization Improved non-dominated sorting genetic algorithm II Unconventional oil reservoir
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Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades 认领 引用 被引量：34
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作者 王珑 王同光 罗源 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2011年第6期739-748,共10页
The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an exa... The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines. 展开更多
关键词 wind turbine multi-objective optimization Pareto-optimal solution non-dominated sorting genetic algorithm （NSGA）-II
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Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-II 认领 引用 被引量：4
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作者 Xi JIN Jie ZHANG +1 位作者 Jin-liang GAO Wen-yan WU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2008年第3期391-400,共10页
Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Algorithm-II (NSGA-II) can be used to sol... Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Algorithm-II (NSGA-II) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-II into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by in-troduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated;this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions. 展开更多
关键词 Water supply system Water supply network Optimal rehabilitation Multi-objective Non-dominated sorting Ge-netic Algorithm （NSGA）
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A decoupled multi-objective optimization algorithm for cut order planning of multi-color garment 认领 引用
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作者 DONG Hui LYU Jinyang +3 位作者 LIN Wenjie WU Xiang WU Mincheng HUANG Guangpu 《High Technology Letters》 EI CAS 2025年第1期53-62,共10页
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish... This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises. 展开更多
关键词 multi-objective optimization non-dominated sorting in genetic algorithmsⅡ(NSGAII) cut order planning(COP) multi-color garment linear programming decoupling strategy
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Accelerating Large-Scale Sorting through Parallel Algorithms 认领 引用
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作者 Yahya Alhabboub Fares Almutairi +3 位作者 Mohammed Safhi Yazan Alqahtani Adam Almeedani Yasir Alguwaifli 《Journal of Computer and Communications》 2024年第1期131-138,共8页
This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison ... This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases. 展开更多
关键词 Sorting Algorithm Quick Sort QuickSort Parallel Parallel Algorithms
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Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm 认领 引用 被引量：1
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作者 Xiaocong Zhou Wei Zheng +2 位作者 Zhaowei Li Panlong Wu Yongjin Sun 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第2期285-296,共12页
This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi... This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results. 展开更多
关键词 Depth Sorting Fast Search algorithm Underwater gravity-aided navigation Path planning efficiency Quick Rapidly-exploring Random Trees*(QRRT*)
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An improved non-dominated sorting biogeography-based optimization algorithm for multi-objective land-use allocation:a case study in Kigali-Rwanda 认领 引用 被引量：2
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作者 Olive Niyomubyeyi Mozafar Veysipanah +2 位作者 Sam Sarwat Petter Pilesjö Ali Mansourian 《Geo-Spatial Information Science》 SCIE EI CSCD 2024年第4期968-982,共15页
With the continuous increase of rapid urbanization and population growth,sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers.Multi-objective land-... With the continuous increase of rapid urbanization and population growth,sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers.Multi-objective land-use allocation can be regarded as a complex spatial optimization problem that aims to achieve the possible trade-offs among multiple and conflicting objectives.This paper proposes an improved Non-dominated Sorting Biogeography-Based Optimization(NSBBO)algorithm for solving the multi-objective land-use allocation problem,in which maximum accessibility,maximum compactness,and maximum spatial integration were formulated as spatial objectives;and space syntax analysis was used to analyze the potential movement patterns in the new urban planning area of the city of Kigali,Rwanda.Efficient Non-dominated Sorting(ENS)algorithm and crossover operator were integrated into classical NSBBO to improve the quality of non-dominated solutions,and local search ability,and to accelerate the convergence speed of the algorithm.The results showed that the proposed NSBBO exhibited good optimal solutions with a high hypervolume index compared to the classical NSBBO.Furthermore,the proposed algorithm could generate optimal land use scenarios according to the preferred objectives,thus having the potential to support the decision-making of urban planners and stockholders in revising and updating the existing detailed master plan of land use. 展开更多
关键词 Multi-objective land-use allocation spatial optimization sustainable urban planning Non-dominated Sorting Biogeography-Based Optimization(NSBBO)algorithm
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A multi-line laser scanning system design and evaluation framework based on physical simulation 认领 引用
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作者 Yunpeng LI Yifan CHEN +3 位作者 Limei SONG Hongyi WANG Hongmin WANG Baozhen GE 《Optoelectronics Letters》 EI 2026年第5期268-274,共7页
We propose a multi-line laser simulation system utilizing computer graphics and physical simulation to generate virtual multi-line laser datasets.Our framework provides key physical properties of the scene,including c... We propose a multi-line laser simulation system utilizing computer graphics and physical simulation to generate virtual multi-line laser datasets.Our framework provides key physical properties of the scene,including camera parameters,depth values,surface normals,and the actual two-dimensional(2D)and three-dimensional(3D)coordinates of the laser stripe centers for each rendered image.Beyond,we construct a virtual line laser scanning image dataset with a complex background by simulating interactions between lasers and object surfaces with the Monte Carlo method.With the proposed framework and dataset,a multi-line laser extraction algorithm based on a robust sorting algorithm is proposed and tested,which utilizes distance-based error analysis,connected component labeling,and iterative optimization refinement techniques.Both simulation and actual experiments show that our method outperforms the other state-of-the-art multi-line laser stripe center extraction methods.The proposed framework can be applied to different types of laser scanning systems in the future. 展开更多
关键词 physical simulation computer graphics Robust sorting algorithm Monte Carlo method Multi line laser scanning simulating interactions las virtual line laser scanning image dataset
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Two-stage two-dimensional force allocation strategy for tunnel boring machines based on a region-reconfigurable thrust system 认领 引用
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作者 Zhe ZHENG Kaihao ZHU +7 位作者 Jiaqi HOU Haibo XIE Lijie JIANG Fulong LIN Lianhui JIA Laikuang LIN Huayong YANG Dong HAN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第6期583-597,I0035-I0042,共15页
Determining the group forces of the thrust system is essential for trajectory control of tunnel boring machines(TBMs).Existing methods for selecting an optimal solution mainly consider the force variance among groups,... Determining the group forces of the thrust system is essential for trajectory control of tunnel boring machines(TBMs).Existing methods for selecting an optimal solution mainly consider the force variance among groups,while ignoring other constraints,such as uneven segment loading and excessive hydraulic shock.In this study,we develop a more comprehensive and robust framework for force allocation.First,a novel region-reconfigurable hydraulic system is designed,which enforces consistency among the forces acting on each segment.Then on this basis,for the ramping-up tunneling stage,quadratic programming(QP)is used to optimize force uniformity across the spatial dimension.Compared to the on-site allocation result,the improvement in force uniformity reaches up to 32.89%.Moreover,to address the hydraulic shock caused by excessive adjustment to the force,hydraulic compliance is introduced and optimized together with force uniformity using the non-dominated sorting genetic algorithm II(NSGA-II),which outperforms weighted QP by 1.25×106 kN2 in uniformity and 2.86 kN2 in compliance.Analyzing performance in the steady tunneling stage,the service life of the components improves significantly.To avoid a non-existent solution for the thrust force vector,a genetic algorithm-based error tolerance method is developed.Therefore,all deviation rectification commands can be answered with a minor compromise of up to 3%in the fitting accuracy of the thrust force vector.In summary,this framework enhances the adaptability and robustness of the force allocation strategy,providing a reliable foundation for TBM trajectory control. 展开更多
关键词 Tunnel boring machine(TBM) Thrust system Thrust force vector Force allocation Quadratic programming(QP) Non-dominated sorting genetic algorithm II(NSGA-II)
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MAV-UAV combat organization's force formation plan generation based on NSGA-Ⅲ 认领 引用
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作者 ZHONG Yun WAN Lujun ZHANG Jieyong 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期307-317,共11页
Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation ... Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments. 展开更多
关键词 manned-unmanned aerial vehicle combat organization force formation plan command and decision-making capability resource capability non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)
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Performance analysis and optimization of staggered fin heat exchangers under varying altitudes using machine learning 认领 引用
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作者 Kai ZHAO Chunming SHAO +5 位作者 Xiaoxia SUN Yuanqing XIA Qiangqiang LI Lili SHEN Min LIN Zhi LI 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第8期871-884,I0030-I0037,共14页
In this study,we conduct a comprehensive investigation into the flow and heat transfer behaviors of staggered fin heat dissipation channels across varying altitudes(0-5000 m).The results reveal that higher altitudes l... In this study,we conduct a comprehensive investigation into the flow and heat transfer behaviors of staggered fin heat dissipation channels across varying altitudes(0-5000 m).The results reveal that higher altitudes lead to a notable deterioration in heat exchanger performance.Specifically,compared to sea-level conditions,elevating the altitude to 5000 m results in a concurrent reduction of 23%in the pressure drop and 18%in the heat transfer coefficient.Therefore,while existing fin structures meet low-altitude requirements,they require optimization to adapt to high-altitude environments.However,this optimization process involves evaluating a vast number of design schemes.Traditional computational fluid dynamics(CFD)simulations are often too computationally expensive for this task,creating a significant bottleneck.To address this challenge,we established an efficient optimization framework that integrates numerical simulations,machine learning,and an improved nondominated sorting genetic algorithm II(NSGA-II).Three machine learning models were evaluated,among which the gradient boosting decision tree(GBDT)achieved superior predictive accuracy(determination coefficient R2≈1.0)for both the heat transfer coefficient and pressure drop.Subsequently,multi-objective optimization was realized utilizing GBDT as a surrogate model coupled with the improved NSGA-II.We find that when the pressure drop is comparable to that of the original design,the heat transfer coefficient increases by approximately 23%across all tested altitudes.Conversely,when the heat transfer coefficient remains on par with the original design,the pressure drop decreases by approximately 17%.These findings may help guide the optimal design of next-generation staggered fin heat exchangers suitable for high altitudes. 展开更多
关键词 Staggered fins High altitude Machine learning Nondominated sorting genetic algorithm II(NSGA-II)
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Optimization of airfoil geometry using the NSGA-Ⅱ method to improve the performance and efficiency of a very low head turbine 认领 引用
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作者 Ridwan Arief Subekti Fazila Mohd-Zawawi +7 位作者 Kamarulafizam Ismail Derren Audric Sudarto Muhammad Lucky Witjaksono Qidun Maulana Binu Soesanto Anjar Susatyo Henny Sudibyo Ahmad Fudholi Rudi Darussalam 《Clean Energy》 EI CSCD 2026年第3期127-142,共16页
Research on the development of very low head turbines is becoming increasingly important for harnessing the potential of hydropower in flat-bottomed rivers.However,studies on very low head turbine runner optimization ... Research on the development of very low head turbines is becoming increasingly important for harnessing the potential of hydropower in flat-bottomed rivers.However,studies on very low head turbine runner optimization are generally limited to single-objective approaches or have not specifically integrated airfoil parameterization and aerodynamic evaluation within a multiobjective framework.This study proposed the optimization of the very low head propeller turbine runner airfoil geometry using the nondominated sorting genetic algorithmⅡmultiobjective framework,which combines class shape transformation,genetic algorithm,and XFOIL.The turbine was designed for a net head of 2.07 m,a flow rate of 0.04 m3/s,and a rotational speed of 1700 rpm(high-speed micro-propeller turbine).Optimization was performed on five runner segments to improve the airfoil’s hydrodynamic characteristics,which were subsequently validated using three-dimensional computational fluid dynamics simulations.The results showed that optimization increased the lift-to-drag ratio to 27.67%over the low angle of attack range relevant to operating conditions.At the design point,the optimized runner achieved an efficiency of 86.8%and a power output of∼573 W,which is 1.48%higher than the initial design.These results demonstrate that the nondominated sorting genetic algorithmⅡ-based class shape transformation-genetic algorithm-XFOIL framework is effective for improving the performance of very low head turbine runners. 展开更多
关键词 airfoil design robust optimization very low head turbine nondominated sorting genetic algorithm
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Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem 认领 引用 被引量：13
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作者 Ziyan Zhao Shixin Liu +1 位作者 MengChu Zhou Abdullah Abusorrah 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第6期1199-1209,共11页
Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-de... Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems. 展开更多
关键词 Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II(NSGA-II) production scheduling
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Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms 认领 引用 被引量：9
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作者 JoséD. MARTíNEZ-MORALES Elvia R. PALACIOS-HERNáNDEZ Gerardo A. VELáZQUEZ-CARRILLO 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2013年第9期657-670,共14页
In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （S... In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively. 展开更多
关键词 Engine calibration Multi-objective optimization Neural networks Multiple objective particle swarm optimization（MOPSO） Nondominated sorting genetic algorithm II （NSGA-II）
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Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis 认领 引用 被引量：6
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作者 石磊 姚平经 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2001年第2期173-178,共6页
Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitnes... Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis. 展开更多
关键词 multi-objective programming multi-objective evolutionary algorithm steady-state non-dominated sorting genetic algorithm process synthesis
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Models for Location Inventory Routing Problem of Cold Chain Logistics with NSGA-Ⅱ Algorithm 认领 引用 被引量：3
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作者 郑建国 李康 伍大清 《Journal of Donghua University(English Edition)》 CAS 2017年第4期533-539,共7页
In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location... In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem. 展开更多
关键词 cold chain logistics multi-objective location inventory routing problem(LIRP) non-dominated sorting in genetic algorithm Ⅱ(NSGA-Ⅱ)
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Multiobjective Optimization of Hull Form Based on Global Optimization Algorithm 认领 引用 被引量：1
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作者 LIU Jie ZHANG Baoji 《Journal of Shanghai Jiaotong university(Science)》 EI 2022年第3期346-355,共10页
Rankine source method,optimization technology,parametric modeling technology,and improved multiobjective optimization algorithm were combined to investigate the multiobjective optimization design of hull form.A multio... Rankine source method,optimization technology,parametric modeling technology,and improved multiobjective optimization algorithm were combined to investigate the multiobjective optimization design of hull form.A multiobjective and multilevel optimization design framework was constructed for the comprehensive navigation performance of ships.CAESES software was utilized as the optimization platform,and nondominated sorting genetic algorithm II(NSGA-II)was used to conduct multiobjective optimization research on the resistance and sea-keeping performance of the ITTC Ship A-2 fishing vessel.Optimization objectives of this study are heave/pitch response amplitude and wave-making resistance.Taking the displacement and the length between perpendiculars as constraints,we optimized the profile of the hull.Analytic hierarchy process(AHP)and technique for order preference by similarity to ideal solution(TOPSIS)were used to sort and select Pareto solutions and determine weight coefficient of each navigation performance objective in the general objective.Finally,the hydrodynamic performance before and after the parametric deformation of the hull was compared.The results show that both the wave-making resistance and heave/pitch amplitude of the optimized hull form are reduced,and the satisfactory optimal hull form is obtained.The results of this study have a certain reference value for the initial stage of multiobjective optimization design of hull form. 展开更多
关键词 multiobjective optimization Rankine source method global optimization algorithm nondominated sorting genetic algorithm II(NSGA-II)
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