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Optimization of Blade Geometry of Savonius Hydrokinetic Turbine Based onGenetic Algorithm
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作者 Jiahao Lu Fangfang Zhang +4 位作者 Weilong Guang Yanzhao Wu Ran Tao Xiaoqin Li Ruofu Xiao 《Energy Engineering》 EI 2023年第12期2819-2837,共19页
Savonius hydrokinetic turbine is a kind of turbine set which is suitable for low-velocity conditions.Unlike conventional turbines,Savonius turbines employ S-shaped blades and have simple internal structures.Therefore,... Savonius hydrokinetic turbine is a kind of turbine set which is suitable for low-velocity conditions.Unlike conventional turbines,Savonius turbines employ S-shaped blades and have simple internal structures.Therefore,there is a large space for optimizing the blade geometry.In this study,computational fluid dynamics(CFD)numerical simulation and genetic algorithm(GA)were used for the optimal design.The optimization strategies and methods were determined by comparing the results calculated by CFD with the experimental results.The weighted objective function was constructed with the maximum power coefficient Cp and the high-power coefficient range R under multiple working conditions.GA helps to find the optimal individual of the objective function.Compared the optimal scheme with the initial scheme,the overlap ratioβincreased from 0.2 to 0.202,and the clearance ratioεincreased from 0 to 0.179,the blade circumferential angleγincreased from 0°to 27°,the blade shape extended more towards the spindle.The overall power of Savonius turbines was maintained at a high level over 22%,R also increased from 0.73 to 1.02.In comparison with the initial scheme,the energy loss of the optimal scheme at high blade tip speed is greatly reduced,and this reduction is closely related to the optimization of blade geometry.As R becomes larger,Savonius turbines can adapt to the overall working conditions and meet the needs of its work in low flow rate conditions.The results of this paper can be used as a reference for the hydrodynamic optimization of Savonius turbine runners. 展开更多
关键词 Hydrokinetic turbine savonius runner multiple target optimization genetic algorithm performance improvement
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A combination algorithm of Chaos optimization and genetic algorithm and its application in maneuvering multiple targets data association
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作者 王建华 张琳 刘维亭 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2007年第4期470-473,共4页
The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to de... The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones. 展开更多
关键词 data association chaos optimization genetic algorithm maneuvering multiple targets tracking
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Research on Flexible Job Shop Scheduling Based on Improved Two-Layer Optimization Algorithm
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作者 Qinhui Liu Laizheng Zhu +2 位作者 Zhijie Gao Jilong Wang Jiang Li 《Computers, Materials & Continua》 SCIE EI 2024年第1期811-843,共33页
To improve the productivity,the resource utilization and reduce the production cost of flexible job shops,this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization p... To improve the productivity,the resource utilization and reduce the production cost of flexible job shops,this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching.Firstly,a mathematical model is established to minimize the maximum completion time.Secondly,an improved two-layer optimization algorithm is designed:the outer layer algorithm uses an improved PSO(Particle Swarm Optimization)to solve the workpiece batching problem,and the inner layer algorithm uses an improved GA(Genetic Algorithm)to solve the dual-resource scheduling problem.Then,a rescheduling method is designed to solve the task disturbance problem,represented by machine failures,occurring in the workshop production process.Finally,the superiority and effectiveness of the improved two-layer optimization algorithm are verified by two typical cases.The case results show that the improved two-layer optimization algorithm increases the average productivity by 7.44% compared to the ordinary two-layer optimization algorithm.By setting the different numbers of AGVs(Automated Guided Vehicles)and analyzing the impact on the production cycle of the whole order,this paper uses two indicators,the maximum completion time decreasing rate and the average AGV load time,to obtain the optimal number of AGVs,which saves the cost of production while ensuring the production efficiency.This research combines the solved problem with the real production process,which improves the productivity and reduces the production cost of the flexible job shop,and provides new ideas for the subsequent research. 展开更多
关键词 Dual resource scheduling workpiece batching RESCHEDULING particle swarm optimization genetic algorithm
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Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor 被引量:13
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作者 BOUKHALFA Ghoulemallah BELKACEM Sebti +1 位作者 CHIKHI Abdesselem BENAGGOUNE Said 《Journal of Central South University》 SCIE EI CAS CSCD 2019年第7期1886-1896,共11页
This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different he... This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance. 展开更多
关键词 dual star induction motor drive direct torque control particle swarm optimization (PSO) fuzzy logic control genetic algorithms
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DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization 被引量:3
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作者 Jia-Zhou Liu Zhi-Qin Zhao +1 位作者 Zi-Yuan He Qing-Huo Liu 《Journal of Electronic Science and Technology》 CAS 2014年第1期71-75,共5页
Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival (DOA). In this method, a gen... Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival (DOA). In this method, a genetic algorithm (GA) and fuzzy discrete particle swarm optimization (FDPSO) are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, a chaotic factor and a crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as the Newton-like algorithm, Akaike information critical (AIC), particle swarm optimization (PSO), and genetic algorithm with particle swarm optimization (GA-PSO). 展开更多
关键词 Direction of arrival genetic algorithm particle swarm optimization.
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Optimization Algorithms for Sidelobes SSL Reduction: A Comparative Study
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作者 Mohsen Denden Aymen Alhamdan 《Journal of Computer and Communications》 2024年第7期120-132,共13页
The development of new technologies in smart cities is often hailed as it becomes a necessity to solve many problems like energy consumption and transportation. Wireless networks are part of these technologies but imp... The development of new technologies in smart cities is often hailed as it becomes a necessity to solve many problems like energy consumption and transportation. Wireless networks are part of these technologies but implementation of several antennas, using different frequency bandwidths for many applications might introduce a negative effect on human health security. In wireless networks, most antennas generate sidelobes SSL. SSL causes interference and can be an additional resource for RF power that can affect human being health. This paper aims to study algorithms that can reduce SSL. The study concerns typical uniform linear antenna arrays. Different optimum side lobe level reduction algorithms are presented. Genetic algorithm GA, Chebyshev, and Particle Swarm Optimization algorithm are used in the optimization process. A comparative study between the indicated algorithms in terms of stability, precision, and running time is shown. Results show that using these algorithms in optimizing antenna parameters can reduce SSL. A comparison of these algorithms is carried out and results show the difference between them in terms of running time and SSL reduction Level. 展开更多
关键词 SSL Radio Wave Interference SAR Linear Antenna Arrays genetic algorithm CHEBYSHEV Particle swarm optimization
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Robot stereo vision calibration method with genetic algorithm and particle swarm optimization 被引量:1
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作者 汪首坤 李德龙 +1 位作者 郭俊杰 王军政 《Journal of Beijing Institute of Technology》 EI CAS 2013年第2期213-221,共9页
Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm (GA) and particle swarm optimization (PSO), a ... Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm (GA) and particle swarm optimization (PSO), a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras' parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion (DLT), GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation. 展开更多
关键词 robot stereo vision camera calibration genetic algorithm (GA) particle swarm opti-mization (PSO) hybrid intelligent optimization
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Optimization of Fairhurst-Cook Model for 2-D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA)
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作者 Mohammad Najjarpour Hossein Jalalifar 《Journal of Applied Mathematics and Physics》 2018年第8期1581-1595,共15页
The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the slid... The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack. 展开更多
关键词 WING Crack Fairhorst-Cook Model Sensitivity Analysis optimization Particle swarm INTELLIGENCE (PSO) Ant Colony optimization (ACO) genetic algorithm (GA)
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Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot
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作者 Rega Rajendra Dilip K. Pratihar 《Intelligent Control and Automation》 2011年第4期430-449,共20页
The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipula... The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected. 展开更多
关键词 MANIPULATOR optimAL Structure Adaptive CONTROLLER genetic algorithm NEURAL Networks Particle swarm optimization
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Bacterial graphical user interface oriented by particle swarm optimization strategy for optimization of multiple type DFACTS for power quality enhancement in distribution system 被引量:3
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作者 M.Mohammadi M.Montazeri S.Abasi 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第3期569-588,共20页
This study proposes a graphical user interface(GUI) based on an enhanced bacterial foraging optimization(EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution syste... This study proposes a graphical user interface(GUI) based on an enhanced bacterial foraging optimization(EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution systems.The proposed GUI based toolbox,allows the user to choose between single and multiple DFACTS allocations,followed by the type and number of them to be allocated.The EBFO is then applied to obtain optimal locations and ratings of the single and multiple DFACTS.This is found to be faster and provides more accurate results compared to the usual PSO and BFO.Results obtained with MATLAB/Simulink simulations are compared with PSO,BFO and enhanced BFO.It reveals that enhanced BFO shows quick convergence to reach the desired solution there by yielding superior solution quality.Simulation results concluded that the EBFO based multiple DFACTS allocation using DSSSC,APC and DSTATCOM is preferable to reduce power losses,improve load balancing and enhance voltage deviation index to 70%,38% and 132% respectively and also it can improve loading factor without additional power loss. 展开更多
关键词 distribution system power quality single type and multiple type DFACTS BFO algorithm particle swarm optimization(PSO)
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HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA 被引量:17
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作者 罗德林 杨忠 +2 位作者 段海滨 吴在桂 沈春林 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2006年第1期20-26,共7页
Combining the heuristic algorithm (HA) developed based on the specific knowledge of the cooperative multiple target attack (CMTA) tactics and the particle swarm optimization (PSO), a heuristic particle swarm opt... Combining the heuristic algorithm (HA) developed based on the specific knowledge of the cooperative multiple target attack (CMTA) tactics and the particle swarm optimization (PSO), a heuristic particle swarm optimization (HPSO) algorithm is proposed to solve the decision-making (DM) problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts (FAs) to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem. 展开更多
关键词 air combat decision-making cooperative multiple target attack particle swarm optimization heuristic algorithm
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Brillouin scattering spectrum character extraction based on genetic algorithm and seeker optimization algorithm
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作者 Zhang Yanjun Jin Peijun +3 位作者 Fu Xinghu Hou Jiaoru Zhang Fangcao Xu Jinrui 《High Technology Letters》 EI CAS 2019年第4期401-407,共7页
A new hybrid optimization method based on genetic algorithm(GA)and seeker optimization algorithm(SOA)is presented in this paper.The hybrid algorithm optimizes SOA by using crossover and mutation operations in GA in or... A new hybrid optimization method based on genetic algorithm(GA)and seeker optimization algorithm(SOA)is presented in this paper.The hybrid algorithm optimizes SOA by using crossover and mutation operations in GA in order to improve the global search ability of SOA.Four algorithms,i.e.particle swarm optimization(PSO),SOA,GA and quantum-behaved particle swarm optimization(GA-QPSO)and GA-SOA are used to process the simulation and experimental data of Brillouin scattering spectrum(BSS)at different temperatures.The results show that GA-SOA improves the accuracy of extracting the center frequency shift and the minimum center frequency of Brillouin scattering spectrum compared with other three algorithms.The shift error is 0.203 MHz.Therefore,GA-SOA can be applied to the accurate extraction of BSS characteristics. 展开更多
关键词 Brillouin scattering spectrum(BSS) seeker optimization algorithm(SOA) genetic algorithm(GA) particle swarm optimization(PSO) Brillouin frequency shift(BFS)
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Planning of DC Electric Spring with Particle Swarm Optimization and Elitist Non-dominated Sorting Genetic Algorithm
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作者 Qingsong Wang Siwei Li +2 位作者 Hao Ding Ming Cheng Giuseppe Buja 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2024年第2期574-583,共10页
This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical... This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical load (NCL) and internal storage. It can offer higher power quality to critical load (CL), reduce power imbalance and relieve pressure on energy storage systems (RESs). In this paper, a planning method for parallel DCESs is proposed to maximize stability gain, economic benefits, and penetration of RESs. The planning model is a master optimization with sub-optimization to highlight the priority of objectives. Master optimization is used to improve stability of the network, and sub-optimization aims to improve economic benefit and allowable penetration of RESs. This issue is a multivariable nonlinear mixed integer problem, requiring huge calculations by using common solvers. Therefore, particle Swarm optimization (PSO) and Elitist non-dominated sorting genetic algorithm (NSGA-II) were used to solve this model. Considering uncertainty of RESs, this paper verifies effectiveness of the proposed planning method on IEEE 33-bus system based on deterministic scenarios obtained by scenario analysis. 展开更多
关键词 DC distribution network DC electric spring non-dominated sorting genetic algorithm particle swarm optimization renewable energy source
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Hybrid Global Optimization Algorithm for Feature Selection 被引量:1
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作者 Ahmad Taher Azar Zafar Iqbal Khan +1 位作者 Syed Umar Amin Khaled M.Fouad 《Computers, Materials & Continua》 SCIE EI 2023年第1期2021-2037,共17页
This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing ... This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features. 展开更多
关键词 Particle swarm optimization(PSO) time-variant acceleration coefficients(TVAC) genetic algorithms differential evolution feature selection medical data
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Optimization of CNC Turning Machining Parameters Based on Bp-DWMOPSO Algorithm
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作者 Jiang Li Jiutao Zhao +3 位作者 Qinhui Liu Laizheng Zhu Jinyi Guo Weijiu Zhang 《Computers, Materials & Continua》 SCIE EI 2023年第10期223-244,共22页
Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImpr... Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters. 展开更多
关键词 Machining parameters Bp neural network multiple Objective Particle swarm optimization Bp-DWMOPSO algorithm
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An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks
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作者 Reem Alkanhel Kalaiselvi Chinnathambi +4 位作者 C.Thilagavathi Mohamed Abouhawwash Mona A.Al duailij Manal Abdullah Alohali Doaa Sami Khafaga 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期1571-1583,共13页
Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings.Designing energy-efficient data gathering methods in l... Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings.Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difficult areas of study.As every sensor node has afinite amount of energy.Battery power is the most significant source in the WSN.Clustering is a well-known technique for enhan-cing the power feature in WSN.In the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing opti-mization.By using distributed data transmission modification,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area.To begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy con-sumption.The Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efficient Cluster Head(CH).It also improves the network’s longevity and optimizes the routing.As a result of the study’sfindings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay. 展开更多
关键词 CLUSTERING energy consumption genetic algorithm multi swarm optimization adaptive hierarchical clustering ROUTING cluster head
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An Effective Non-Commutative Encryption Approach with Optimized Genetic Algorithm for Ensuring Data Protection in Cloud Computing 被引量:2
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作者 S.Jerald Nirmal Kumar S.Ravimaran M.M.Gowthul Alam 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第11期671-697,共27页
Nowadays,succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers.Hence,to secure both data and keys ensuring secured data storag... Nowadays,succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers.Hence,to secure both data and keys ensuring secured data storage and access,our proposed work designs a Novel Quantum Key Distribution(QKD)relying upon a non-commutative encryption framework.It makes use of a Novel Quantum Key Distribution approach,which guarantees high level secured data transmission.Along with this,a shared secret is generated using Diffie Hellman(DH)to certify secured key generation at reduced time complexity.Moreover,a non-commutative approach is used,which effectively allows the users to store and access the encrypted data into the cloud server.Also,to prevent data loss or corruption caused by the insiders in the cloud,Optimized Genetic Algorithm(OGA)is utilized,which effectively recovers the data and retrieve it if the missed data without loss.It is then followed with the decryption process as if requested by the user.Thus our proposed framework ensures authentication and paves way for secure data access,with enhanced performance and reduced complexities experienced with the prior works. 展开更多
关键词 Cloud computing quantum key distribution Diffie Hellman non-commutative approach genetic algorithm particle swarm optimization
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Hybrid Optimization Based PID Controller Design for Unstable System 被引量:1
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作者 Saranya Rajeshwaran C.Agees Kumar Kanthaswamy Ganapathy 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期1611-1625,共15页
PID controllers play an important function in determining tuning para-meters in any process sector to deliver optimal and resilient performance for non-linear,stable and unstable processes.The effectiveness of the pre... PID controllers play an important function in determining tuning para-meters in any process sector to deliver optimal and resilient performance for non-linear,stable and unstable processes.The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller.The Direct Multi Search(DMS)algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model.A Metaheuristics Algorithm such as,SA(Simulated Annealing),MBBO(Modified Biogeography Based Opti-mization),BBO(Biogeography Based Optimization),PBIL(Population Based Incremental Learning),ES(Evolution Strategy),StudGA(Stud Genetic Algo-rithms),PSO(Particle Swarm Optimization),StudGA(Stud Genetic Algorithms),ES(Evolution Strategy),PSO(Particle Swarm Optimization)and ACO(Ant Col-ony Optimization)are used to tune the PID controller and Smith predictor design.The effectiveness of the suggested algorithms DMS-SA,DMS-BBO,DMS-MBBO,DMS-PBIL,DMS-StudGA,DMS-ES,DMS-ACO,and DMS-PSO for a class of dead-time structures employing PID controller and Smith predictor design controllers is illustrated using unit step set point response.When compared to other optimizations,the suggested hybrid metaheuristics approach improves the time response analysis when extended to the problem of smith predictor and PID controller designed tuning. 展开更多
关键词 Direct multi search simulated annealing biogeography-based optimization stud genetic algorithms particle swarm optimization SmithPID controller
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Intelligent Optimization Algorithms to VDA of Models with on/off Parameterizations 被引量:8
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作者 方昌銮 郑琴 +1 位作者 吴文华 戴毅 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2009年第6期1181-1197,共17页
Some variational data assimilation (VDA) problems of time- and space-discrete models with on/off parameterizations can be regarded as non-smooth optimization problems. Same as the sub-gradient type method, intellige... Some variational data assimilation (VDA) problems of time- and space-discrete models with on/off parameterizations can be regarded as non-smooth optimization problems. Same as the sub-gradient type method, intelligent optimization algorithms, which are widely used in engineering optimization, can also be adopted in VDA in virtue of their no requirement of cost function's gradient (or sub-gradient) and their capability of global convergence. Two typical intelligent optimization algorithms, genetic algorithm (GA) and particle swarm optimization (PSO), are introduced to VDA of modified Lorenz equations with on-off parameterizations, then two VDA schemes are proposed, that is, GA based VDA (GA-VDA) and PSO based VDA (PSO-VDA). After revealing the advantage of GA and PSO over conventional adjoint methods in the ability of global searching at the existence of cost function's discontinuity induced by on-off switches, sensitivities of GA-VDA and PSO-VDA to population size, observational noise, model error and observational density are detailedly analyzed. It's shown that, in the context of modified Lorenz equations, with proper population size, GA-VDA and PSO-VDA can effectively estimate the global optimal solution, while PSO-VDA consumes much less computational time than GA-VDA with the same population size, and requires a much lower population size with nearly the same results, both methods are not very sensitive to observation noise and model error, while PSO-VDA shows a better performance with observational noise than GA-VDA. It is encouraging that both methods are not sensitive to observational density, especially PSO-VDA, using which almost the same perfect assimilation results can be obtained with comparatively sparse observations. 展开更多
关键词 ON-OFF genetic algorithm particle swarm optimization variational data assimilation sensitivity
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Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights 被引量:9
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作者 Hai-tao Chen Wen-chuan Wang +1 位作者 Xiao-nan Chen Lin Qiu 《Water Science and Engineering》 EI CAS CSCD 2020年第2期136-144,共9页
Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algori... Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified. 展开更多
关键词 Particle swarm optimization genetic algorithm Random inertia weight Multi-objective reservoir operation Reservoir group Panjiakou Reservoir
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