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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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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
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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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PMS-Sorting:A New Sorting Algorithm Based on Similarity 认领 引用
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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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Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm 认领 引用 被引量：5
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作者 Zhengyu Liu Jue Kou +5 位作者 Zengxin Yan Peilong Wang Chang Liu Chunbao Sun Anlin Shao Bern Klein 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2024年第4期545-556,共12页
X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hi... X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms. 展开更多
关键词 XRF sensor-based sorting PSO-SVM algorithm Copper ore pebble Receiver operating curve(ROC) Net smelter return(NSR)
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Non-dominated Sorting Advanced Butterfly Optimization Algorithm for Multi-objective Problems 认领 引用 被引量：2
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作者 Sushmita Sharma Nima Khodadadi +2 位作者 Apu Kumar Saha Farhad Soleimanian Gharehchopogh Seyedali Mirjalili 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第2期819-843,共25页
This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems.There is also an improvement to the original version of B... This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems.There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version.Due to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance strategy.Seven benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint problems.Various performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance comparison.It is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared quantitatively.From all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence. 展开更多
关键词 Multi-objective problems Butterfly optimization algorithm Non-dominated sorting Crowding distance
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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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 Only-Once-Sorting Algorithm 认领 引用
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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
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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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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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An NC Algorithm for Sorting Real Numbers in O(nlogn/√loglogn) Operations 认领 引用
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作者 Yijie Han Sneha Mishra Md Usman Gani Syed 《Open Journal of Applied Sciences》 2019年第5期403-408,共6页
We apply the recent important result of serial sorting of n real numbers in time to the design of a parallel algorithm for sorting real numbers in time and operations. This is the first NC algorithm known to take oper... We apply the recent important result of serial sorting of n real numbers in time to the design of a parallel algorithm for sorting real numbers in time and operations. This is the first NC algorithm known to take operations for sorting real numbers. 展开更多
关键词 Parallel Algorithms Sorting Sort Real Numbers Complexity
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SORT目标检测算法研究 认领 引用
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作者 朱萌博 《软件》 2026年第2期28-30,共3页
SORT(Simple Online and Realtime Tracking)算法作为一种经典的基于检测的多目标跟踪算法,凭借简单高效、实时性的特点受到广泛关注。本文对SORT目标检测算法进行研究,首先,剖析了算法的核心原理,包括卡尔曼滤波器、匈牙利算法和IOU等... SORT(Simple Online and Realtime Tracking)算法作为一种经典的基于检测的多目标跟踪算法,凭借简单高效、实时性的特点受到广泛关注。本文对SORT目标检测算法进行研究,首先,剖析了算法的核心原理,包括卡尔曼滤波器、匈牙利算法和IOU等关键组件;其次,梳理了算法的完整流程,并进行SORT算法的性能分析与实验验证;最后,梳理了SORT算法的改进思路与优化策略。研究表明,SORT算法在平稳运动目标跟踪场景中表现优异,在目标遮挡、快速运动等复杂场景中仍需改进。未来可通过融合多特征和优化数据关联策略等方式,进一步提升其鲁棒性和准确性。 展开更多
关键词 SORT算法 目标检测 多目标跟踪 卡尔曼滤波器 匈牙利算法
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Modeling and Optimization of Electrical Discharge Machining of SiC Parameters, Using Neural Network and Non-Dominating Sorting Genetic Algorithm (NSGA II) 认领 引用
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作者 Ramezan Ali MahdaviNejad 《Materials Sciences and Applications》 2011年第6期669-675,共7页
Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present... Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present work is aimed to optimize the surface roughness and material removal rate of electro discharge machining of SiC parameters simultaneously. As the output parameters are conflicting in nature, so there is no single combination of machining parameters, which provides the best machining performance. Artificial neural network (ANN) with back propagation algorithm is used to model the process. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time (Toff) on electric discharge machining of SiC are considered. Experiments have been conducted over a wide range of considered input parameters for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work. 展开更多
关键词 Electro Discharge Machining Non-Dominating Sorting Algorithm Neural Network REFEL SiC
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基于Quick Sorting的快速分页排序算法 认领 引用 被引量：1
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作者 杨建武 刘缙 《计算机工程》 EI CAS 北大核心 2005年第4期82-84,共3页
提出了分页排序的概念和基于Quick Sorting的快速分页排序算法(Quick Page Sorting) 以及基于Hint缓存机制的算法实现技术。实验表明,在数万至数百万数据总量情况下,Quick Page Soring的速度比Quick Sorting快10倍左右,大大提高了应用... 提出了分页排序的概念和基于Quick Sorting的快速分页排序算法(Quick Page Sorting) 以及基于Hint缓存机制的算法实现技术。实验表明,在数万至数百万数据总量情况下,Quick Page Soring的速度比Quick Sorting快10倍左右,大大提高了应用系统的响应速度。 展开更多
关键词 排序 分页排序 算法 快速分页排序
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An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ 认领 引用
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作者 Afia Zafar Muhammad Aamir +6 位作者 Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi 《Computers, Materials & Continua》 SCIE EI 2023年第3期5641-5661,共21页
In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural ne... In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature. 展开更多
关键词 Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter optimization
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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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基于改进BOT-Sort算法的多目标追踪方法 认领 引用
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作者 李书钦 王一凡 《北方工业大学学报》 2025年第6期37-48,共12页
针对社区复杂环境下多目标追踪精度低与轨迹连续性差的问题,本文提出一种基于改进Boosted SORT with Stronger ReID(BOT-Sort)的多目标追踪算法,通过在Split-Attention Networks(ResNeSt)不同层级中加入自适应图通道聚合网络,并将其作为... 针对社区复杂环境下多目标追踪精度低与轨迹连续性差的问题,本文提出一种基于改进Boosted SORT with Stronger ReID(BOT-Sort)的多目标追踪算法,通过在Split-Attention Networks(ResNeSt)不同层级中加入自适应图通道聚合网络,并将其作为BOT-Sort算法的特征提取器,提高模型对于行人的全局和局部特征特征提取能力;同时将基于局部-全局上下文的行人重识别(Partial-Global Context Network for Person Re-Identification, PGCID)算法作为BOT-Sort算法的行人重识别模块,提升模型的特征融合能力。基于MOT17数据集对改进模型进行端到端训练,并在MOT17和MOT20数据集上进行对比实验。结果显示,改进的BOT-Sort算法的多目标跟踪精度(Multiple Object Tracking Accuracy, MOTA)指标、识别(Identification F1 Score, IDF1)指标、高阶跟踪精度(Higher Order Tracking Accuracy, HOTA)指标分别达到了80.6%、80.3%和66.2%,追踪目标身份交换次数(Identity Switches, IDsw)降至1 065次,提升了社区复杂场景下多目标追踪的精度与轨迹连续性。 展开更多
关键词 BOT-Sort算法 行人追踪 多目标追踪
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Smart Bubble Sort:A Novel and Dynamic Variant of Bubble Sort Algorithm 认领 引用
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作者 Mohammad Khalid Imam Rahmani 《Computers, Materials & Continua》 SCIE EI 2022年第6期4895-4913,共19页
In the present era,a very huge volume of data is being stored in online and offline databases.Enterprise houses,research,medical as well as healthcare organizations,and academic institutions store data in databases an... In the present era,a very huge volume of data is being stored in online and offline databases.Enterprise houses,research,medical as well as healthcare organizations,and academic institutions store data in databases and their subsequent retrievals are performed for further processing.Finding the required data from a given database within the minimum possible time is one of the key factors in achieving the best possible performance of any computer-based application.If the data is already sorted,finding or searching is comparatively faster.In real-life scenarios,the data collected from different sources may not be in sorted order.Sorting algorithms are required to arrange the data in some order in the least possible time.In this paper,I propose an intelligent approach towards designing a smart variant of the bubble sort algorithm.I call it Smart Bubble sort that exhibits dynamic footprint:The capability of adapting itself from the average-case to the best-case scenario.It is an in-place sorting algorithm and its best-case time complexity isΩ(n).It is linear and better than bubble sort,selection sort,and merge sort.In averagecase and worst-case analyses,the complexity estimates are based on its static footprint analyses.Its complexity in worst-case is O(n2)and in average-case isΘ(n2).Smart Bubble sort is capable of adapting itself to the best-case scenario from the average-case scenario at any subsequent stages due to its dynamic and intelligent nature.The Smart Bubble sort outperforms bubble sort,selection sort,and merge sort in the best-case scenario whereas it outperforms bubble sort in the average-case scenario. 展开更多
关键词 Sorting algorithms smart bubble sort footprint dynamic footprint time complexity asymptotic analysis
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Heap Sorting Based on Array Sorting 认领 引用
20
作者 Haiming Li Ping Chen Yong Wang 《Journal of Computer and Communications》 2017年第12期57-62,共6页
A kind of heap sorting method based on array sorting was proposed. Some advantages and disadvantages of it were discussed. It was compared with the traditional method of direct application. In the method, the ordered ... A kind of heap sorting method based on array sorting was proposed. Some advantages and disadvantages of it were discussed. It was compared with the traditional method of direct application. In the method, the ordered keywords in the array are put into the heap one by one after building an empty heap. This method needs relatively less space and is fit for ordered sequence. 展开更多
关键词 Heap Sort Array Bottom-Up Algorithm
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