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A Comprehensive Review of Sizing and Allocation of Distributed Power Generation:Optimization Techniques,Global Insights,and Smart Grid Implications 认领 引用 被引量：1
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作者 Abdullrahman A.Al-Shamma’a Hassan M.Hussein Farh +4 位作者 Ridwan Taiwo Al-Wesabi Ibrahim Abdulrhman Alshaabani Saad Mekhilef Mohamed A.Mohamed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第11期1303-1347,共45页
Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive ... Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems. 展开更多
关键词 Systematic and scientometric global trends distributed generation sizing and allocation multiobjectives modelling and algorithmic optimization
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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking 认领 引用 被引量：1
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作者 Shu-Chuan Chu Libin Fu +2 位作者 Jeng-Shyang Pan Xingsi Xue Min Liu 《Computers, Materials & Continua》 SCIE EI 2025年第5期3135-3163,共29页
Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programm... Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency. 展开更多
关键词 Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework
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Design strategy of advanced generation breeding population of Pinus tabuliformis based on genetic variation and inbreeding level 认领 引用 被引量：1
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作者 Chengcheng Zhou Fan Sun +2 位作者 Zhiyuan Jiao Yousry A.El-Kassaby Wei Li 《Forest Ecosystems》 SCIE CAS CSCD 2025年第4期607-618,共12页
The level of genetic variation within a breeding population affects the effectiveness of selection strategies for genetic improvement.The relationship between genetic variation level within Pinus tabuliformis breeding... The level of genetic variation within a breeding population affects the effectiveness of selection strategies for genetic improvement.The relationship between genetic variation level within Pinus tabuliformis breeding populations and selection strategies or selection effectiveness is not fully investigated.Here,we compared the selection effectiveness of combined and individual direct selection strategies using half-and full-sib families produced from advanced-generation P.tabuliformis seed orchard as our test populations.Our results revealed that,within half-sib families,average diameter at breast height(DBH),tree height,and volume growth of superior individuals selected by the direct selection strategy were higher by 7.72%,18.56%,and 31.01%,respectively,than those selected by the combined selection strategy.Furthermore,significant differences(P<0.01)were observed between the two strategies in terms of the expected genetic gains for average tree height and volume.In contrast,within full-sib families,the differences in tree average DBH,height,and volume between the two selection strategies were relatively minor with increase of 0.17%,2.73%,and 2.21%,respectively,and no significant differences were found in the average expected genetic gains for the studied traits.Half-sib families exhibited greater phenotypic and genetic variation,resulting in improved selection efficiency with the direct selection strategy but also introduced a level of inbreeding risk.Based on genetic distance estimates using molecular markers,our comparative seed orchard design analysis showed that the Improved Adaptive Genetic Programming Algorithm(IAPGA)reduced the average inbreeding coefficient by 14.36% and 14.73% compared to sequential and random designs,respectively.In conclusion,the combination of the direct selection strategy with IAPGA seed orchard design aimed at minimizing inbreeding offered an efficient approach for establishing advanced-generation P.tabuliformis seed orchards. 展开更多
关键词 Breeding population Parental selection strategies Advanced generation seed orchard Pinus tabuliformis Seed orchard design Improved Adaptive Genetic Programming Algorithm
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Large Language Models for Effective Detection of Algorithmically Generated Domains:A Comprehensive Review 认领 引用 被引量：1
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作者 Hamed Alqahtani Gulshan Kumar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第8期1439-1479,共41页
Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection me... Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems. 展开更多
关键词 Adversarial domains cyber threat detection domain generation algorithms large language models machine learning security
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Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm 认领 引用 被引量：10
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作者 K.Jagatheesan B.Anand +3 位作者 Sourav Samanta Nilanjan Dey Amira S.Ashour Valentina E.Balas 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第2期503-515,共13页
Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system ... Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller. 展开更多
关键词 Automatic generation control(AGC) firefly algorithm genetic algorithm(GA) particle swarm optimization(PSO) proportional-integral-derivative(PID) controller
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Optimising PID Controllers for Multi-Area Automatic Generation Control With Improved NSGA-Ⅱ 认领 引用
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作者 Yang Yang Yuchao Gao +1 位作者 Shangce Gao Jinran Wu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第4期1135-1147,共13页
Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonli... Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios.This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm Ⅱ(SNSGA).The proposed model optimises the PID controller by minimising key performance metrics:integration time squared error(ITSE),integration time absolute error(ITAE),and rate of change of deviation(J).This approach balances convergence rate,overshoot,and oscillation dynamics effectively.A fuzzy-based method is employed to select the most suitable solution from the Pareto set.The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-Ⅱ and other advanced control methods.In a two-area thermal power system without reheat,the SNSGA significantly reduces settling times for frequency deviations:2.94s for Δf1 and 4.98s for Δf2,marking improvements of 31.6%and 13.4%over NSGA-Ⅱ,respectively. 展开更多
关键词 automatic generation control load frequency control multi-objective optimization nondominated sorting genetic algorithmⅡ PID controller
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On the Generation Algorithm of VRS Virtual Observations 认领 引用 被引量：3
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作者 WEI Erhu CHAI Hua +1 位作者 LIU Jingnan AN Zhiguo 《Geo-Spatial Information Science》 EI 2007年第2期91-95,共5页
In the past few years, network RTK positioning technology, especially the VRS technology, has been widely used in some parts of China and in many countries around the world. The principle of the VRS technology is disc... In the past few years, network RTK positioning technology, especially the VRS technology, has been widely used in some parts of China and in many countries around the world. The principle of the VRS technology is discussed with corresponding formula deduction, and detailed descriptions and applications of VRS corrections and virtual observations generation algorithm are given. 展开更多
关键词 network-RTK MRS corrections generation algorithm virtual observation
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Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm 认领 引用 被引量：3
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作者 Wei XIE Chang-ming JI +1 位作者 Zi-jun YANG Xiao-xing ZHANG 《Water Science and Engineering》 EI CAS 2012年第1期46-58,共13页
Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity... Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching. 展开更多
关键词 scheduling rule short-time power generation dispatching, hybrid algorithm cascade hydropower station
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Improving PID Controller Performance in Nonlinear Oscillatory Automatic Generation Control Systems Using a Multi-objective Marine Predator Algorithm with Enhanced Diversity 认领 引用 被引量：1
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作者 Yang Yang Yuchao Gao +2 位作者 Jinran Wu Zhe Ding Shangrui Zhao 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第5期2497-2514,共18页
Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy... Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy to improve the opti-mization of PID controllers within nonlinear oscillatory Automatic Generation Control(AGC)systems,essential for the stability of power systems.Our approach aims to reduce the integrated time squared error,the integrated time absolute error,and the rate of change in deviation,facilitating faster convergence,diminished overshoot,and decreased oscillations.By incorporating the spiral model from the Whale Optimization Algorithm(WOA)into the Multi-Objective Marine Predator Algorithm(MOMPA),our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies.Furthermore,the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima,thereby generating optimal Pareto solutions.When applied to nonlinear AGC systems featuring governor dead zones,the PID controllers optimized by QQSMOMPA not only achieve 14%reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs. 展开更多
关键词 Multi-objective optimization Automatic generation control PID controller Multi-objective marine predator algorithm Whale optimization algorithm
Optimal Scheduling Method of Cogeneration System with Heat Storage Device Based on Memetic Algorithm 认领 引用 被引量：2
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作者 Haibo Li YibaoWang +2 位作者 Xinfu Pang Wei Liu Xu Zhang 《Energy Engineering》 EI 2023年第2期317-343,共27页
Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area.To imp... Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area.To improve the wind-power absorption capacity and operating economy of the system,the structure of the system is improved by adding a heat storage device and an electric boiler.First,aiming at the minimum operating cost of the system,the optimal scheduling model of the cogeneration system,including a heat storage device and electric boiler,is constructed.Second,according to the characteristics of the problem,a cultural gene algorithm program is compiled to simulate the calculation example.Finally,through the system improvement,the comparison between the conditions before and after and the simulation solutions of similar algorithms prove the effectiveness of the proposed scheme.The simulation results show that adding the heat storage device and electric boiler to the scheduling optimization process not only improves the wind power consumption capacity of the cogeneration system but also reduces the operating cost of the system by significantly reducing the coal consumption of the unit and improving the economy of the system operation.The cultural gene algorithm framework has both the global evolution process of the population and the local search for the characteristics of the problem,which has a better optimization effect on the solution. 展开更多
关键词 Combined heat and power generation heat storage device memetic algorithm simulated annealing wind abandonment
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Solving open vehicle problem with time window by hybrid column generation algorithm 认领 引用 被引量：1
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作者 YU Naikang QIAN Bin +2 位作者 HU Rong CHEN Yuwang WANG Ling 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2022年第4期997-1009,共13页
This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the ... This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process.We propose a hybrid column generation algorithm(HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time. 展开更多
关键词 open vehicle routing problem with time window(OVRPTW) hybrid column generation algorithm(HCGA) mixed integer programming label setting algorithm
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ALGORITHMS FOR TETRAHEDRAL NETWORK(TEN) GENERATION 认领 引用 被引量：11
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作者 LI Qingquan LI Deren 《Geo-Spatial Information Science》 2000年第1期11-16,共6页
The Tetrahedral Network(TEN) is a powerful 3-D vector structure in GIS, which has a lot of advantages such as simple structure, fast topological relation processing and rapid visualization. The difficulty of TEN appli... The Tetrahedral Network(TEN) is a powerful 3-D vector structure in GIS, which has a lot of advantages such as simple structure, fast topological relation processing and rapid visualization. The difficulty of TEN application is automatic creating data structure. Although a raster algorithm has been introduced by some authors, the problems in accuracy, memory requirement, speed and integrity are still existent. In this paper, the raster algorithm is completed and a vector algorithm is presented after a 3-D data model and structure of TEN have been introducted. Finally, experiment, conclusion and future work are discussed. 展开更多
关键词 3-D GIS tetrahedral network(TEN) generation algorithm
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Advanced in power control for distributed generation system using cooperative algorithms 认领 引用
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作者 王爱华 狄建雄 陈耀斌 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2008年第6期878-883,共6页
To develop efficient power control strategies for a distributed generation system in order to improve the overall system efficiency, we propose a cooperative algorithm to analyze and design the controller, in which el... To develop efficient power control strategies for a distributed generation system in order to improve the overall system efficiency, we propose a cooperative algorithm to analyze and design the controller, in which elements of conventional mathematical optimization algorithms are combined with adaptive dynamic elements drawn from intelligent control theory. In our design, the sequential quadratic programming algorithm was first utilized to obtain an optimal solution for power distribution among multiple units. Fuzzy system was then developed to implement the optimal strategies on the basis of optimal solution. In addition, parameters of the fuzzy system were adapted via a genetic algorithm. Tbe simulation results illustrate that the methodology described is useful for a range of control system designs. 展开更多
关键词 distributed generation power control optimization fuzzy logic genetic algorithm
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Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation 认领 引用
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作者 Caixia Tao Shize Yang Taiguo Li 《Energy Engineering》 EI 2024年第1期187-201,共15页
With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization p... With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability. 展开更多
关键词 Reconfiguration of distribution network distributed generation particle swarm optimization algorithm simulated annealing algorithm active network loss
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A Cuckoo Search Detector Generation-based Negative Selection Algorithm 认领 引用
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作者 Ayodele Lasisi Ali M.Aseere 《Computer Systems Science & Engineering》 SCIE EI 2021年第8期183-195,共13页
The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificia... The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificial immune system.A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities.However,these detectors have limited performance.Redundant detectors are generated,leading to difficulties for detectors to effectively occupy the non-self space.To alleviate this problem,we propose the nature-inspired metaheuristic cuckoo search(CS),a stochastic global search algorithm,which improves the random generation of detectors in the NSA.Inbuilt characteristics such as mutation,crossover,and selection operators make the CS attain global convergence.With the use of Lévy flight and a distance measure,efficient detectors are produced.Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA,with an average increase of 3.52%detection rate on the tested datasets.The proposed method shows superiority over other models,and detection rates of 98%and 99.29%on Fisher’s IRIS and Breast Cancer datasets,respectively.Thus,the generation of highest detection rates and lowest false alarm rates can be achieved. 展开更多
关键词 Negative selection algorithm detector generation cuckoo search optimization
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Non-dominated sorting culture differential evolution algorithm for multi-objective optimal operation of Wind-Solar-Hydro complementary power generation system 认领 引用 被引量：4
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作者 Guanjun Liu Hui Qin +2 位作者 Rui Tian Lingyun Tang Jie Li 《Global Energy Interconnection》 2019年第4期368-374,共7页
Due to the intermittency and instability of Wind-Solar energy and easy compensation of hydropower, this study proposes a Wind-Solar-Hydro power optimal scheduling model. This model is aimed at maximizing the total sys... Due to the intermittency and instability of Wind-Solar energy and easy compensation of hydropower, this study proposes a Wind-Solar-Hydro power optimal scheduling model. This model is aimed at maximizing the total system power generation and the minimum ten-day joint output. To effectively optimize the multi-objective model, a new algorithm named non-dominated sorting culture differential evolution algorithm(NSCDE) is proposed. The feasibility of NSCDE was verified through several well-known benchmark problems. It was then applied to the Jinping Wind-Solar-Hydro complementary power generation system. The results demonstrate that NSCDE can provide decision makers a series of optimized scheduling schemes. 展开更多
关键词 Wind-Solar-Hydro complementary power generation system Scheduling strategy Multi-objective optimization Culture algorithm
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Test Case Generation from UML-Diagrams Using Genetic Algorithm 认领 引用 被引量：2
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作者 Rajesh Kumar Sahoo Morched Derbali +3 位作者 Houssem Jerbi Doan Van Thang P.Pavan Kumar Sipra Sahoo 《Computers, Materials & Continua》 SCIE EI 2021年第5期2321-2336,共16页
Software testing has been attracting a lot of attention for effective software development.In model driven approach,Unified Modelling Language(UML)is a conceptual modelling approach for obligations and other features ... Software testing has been attracting a lot of attention for effective software development.In model driven approach,Unified Modelling Language(UML)is a conceptual modelling approach for obligations and other features of the system in a model-driven methodology.Specialized tools interpret these models into other software artifacts such as code,test data and documentation.The generation of test cases permits the appropriate test data to be determined that have the aptitude to ascertain the requirements.This paper focuses on optimizing the test data obtained from UML activity and state chart diagrams by using Basic Genetic Algorithm(BGA).For generating the test cases,both diagrams were converted into their corresponding intermediate graphical forms namely,Activity Diagram Graph(ADG)and State Chart Diagram Graph(SCDG).Then both graphs will be combined to form a single graph called,Activity State Chart Diagram Graph(ASCDG).Both graphs were then joined to create a single graph known as the Activity State Chart Diagram Graph(ASCDG).Next,the ASCDG will be optimized using BGA to generate the test data.A case study involving a withdrawal from the automated teller machine(ATM)of a bank was employed to demonstrate the approach.The approach successfully identified defects in various ATM functions such as messaging and operation. 展开更多
关键词 Genetic algorithm generation of test data and optimization state-chart diagram activity diagram model-driven approach
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Maximum Power Point Tracking Control of Offshore Wind-Photovoltaic Hybrid Power Generation System with Crane-Assisted 认领 引用
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作者 Xiangyang Cao Yaojie Zheng +1 位作者 Hanbin Xiao Min Xiao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期289-334,共46页
This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mod... This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode control.The algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low jitter.An offshore wind power generation system model is presented to verify the algorithm effect.An offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/Simulink.Compared with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational efficiency.Finally,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation platform.The results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system. 展开更多
关键词 Offshore wind power generation efficiency maximum power point tracking(MPPT) integral sliding mode control grey wolf optimization algorithm offshore photovoltaic cells
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New Solution Generation Strategy to Improve Brain Storm Optimization Algorithm for Classification 认领 引用
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作者 Yu Xue Yan Zhao 《Journal on Internet of Things》 2021年第3期109-118,共10页
As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimizat... As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems. 展开更多
关键词 Brain storm optimization(BSO)algorithm classification generation strategy evolutionary classification optimization
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Biologically Inspired Node Generation Algorithm for Path Planning of Hyper-redundant Manipulators Using Probabilistic Roadmap 认领 引用 被引量：2
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作者 Eric Lanteigne Amor Jnifene 《International Journal of Automation and computing》 CSCD 2014年第2期153-161,共9页
This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration spa... This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator. 展开更多
关键词 Path planning hyper-redundant manipulators probabilistic road map(PRM) quasi-deterministic node generation bi-directional search algorithm.
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