In the field of optimizationalgorithms, hybrid algorithms are increasingly valued by researchers for their effectiveness in improving algorithmic *** recent years, a new type of natural meta-heuristic algorithm calle...
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ISBN:
(纸本)9781665426558
In the field of optimizationalgorithms, hybrid algorithms are increasingly valued by researchers for their effectiveness in improving algorithmic *** recent years, a new type of natural meta-heuristic algorithm called whale optimizationalgorithm has been proposed. The algorithm refers to whales in nature and imitates their three different feeding methods to solve realistic optimization problems. The particleswarmalgorithm, on the other hand, is an algorithm proposed by imitating the way a flock of birds transmits information. As population intelligence algorithms, the accuracy of these two algorithms are not high enough in the convergence process. At the same time, they tend to fall into the local optimum. In this paper, a hybrid algorithm based on whale optimizationalgorithm and particleswarmalgorithm is proposed to update the population by a kind of selection iteration. The experimental results confirm that the algorithm has excellent superiority in convergence accuracy and convergence speed.
In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suctio...
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ISBN:
(纸本)9783038350095
In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suction/injection as well as a reverse flow boundary conditions. A improved particle swarm optimization algorithm (ISPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.
In this paper, through the analysis of the characteristics of particle swarm optimization algorithm, combined with the specific circumstances of Bayesian network structure learning, proposed to based on improved parti...
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ISBN:
(纸本)9783037859926
In this paper, through the analysis of the characteristics of particle swarm optimization algorithm, combined with the specific circumstances of Bayesian network structure learning, proposed to based on improved particleswarmalgorithm. The algorithm uses the BIC measure function as a standard Bayesian network, while preserving the optimal particle case, the possibility of a mutation operation is added to decrease the algorithm into a local optimum. Through a typical Asia network, show that the algorithm is feasible, and other related algorithm is better than the experiment, the effectiveness of the algorithm. In this paper, the algorithm is verified from two aspects of theory and experiments, the results show that the algorithm is feasible.
The proportion of different AGC unit under the shortfall of power in the grid is investigated. The paper adopt PSO algorithm to optimize units of AGC (automatic generation control) which regulate the distribution of p...
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ISBN:
(纸本)9781424428946
The proportion of different AGC unit under the shortfall of power in the grid is investigated. The paper adopt PSO algorithm to optimize units of AGC (automatic generation control) which regulate the distribution of power and be in CPS controlling strategy. The optimal solution is hard to obtain by using average distribution but can be worked by using standard PSO algorithm, and then the CPS level can achieve the optimal under economic conditions. The paper proved the effectiveness of the algorithm by computing example simulation.
As the demand for alternative energy sources grows, wind energy systems are developing at a rapid pace. In this research paper, optimally tuned PI controllers are used for controlling a wind turbine based on of a Doub...
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ISBN:
(纸本)9798350349740;9798350349757
As the demand for alternative energy sources grows, wind energy systems are developing at a rapid pace. In this research paper, optimally tuned PI controllers are used for controlling a wind turbine based on of a Doubly Fed Induction Generator (DFIG). The particleswarmoptimization (PSO) algorithm for PI controller parameters tuning, with the objective of ensuring better energy quality and system performance. The speed Integral Square Error is used as a fitness function of the PSO. The performance evaluation of controllers with PSO implementation is carried out by the simulation through MATLAB /Simulink under linear and nonlinear load conditions. The PI and PI-PSO implementation results comparison proves the superiority of the PI-PSO controller.
Tomato is the main cultivated crop in China, and greenhouse tomato can improve the quality and yield of tomato by improving the planting environment. Predicting greenhouse tomato yield is an important basis for making...
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ISBN:
(纸本)9798350387780;9798350387797
Tomato is the main cultivated crop in China, and greenhouse tomato can improve the quality and yield of tomato by improving the planting environment. Predicting greenhouse tomato yield is an important basis for making production plan, and the accuracy of yield prediction directly affects economic benefits. In order to improve the accuracy of the prediction intervals model of greenhouse tomato yield, a new objective function was proposed to improve the Lower Upper Bound Estimation (LUBE) prediction intervals model. Then, based on the improved LUBE prediction intervals model, the particle swarm optimization algorithm (PSO) was further improved by adaptive inertia weight and escape strategy. Finally, a LUBE prediction intervals model based on the improved PSO algorithm was proposed in this paper. The prediction intervals of greenhouse tomato yield was simulated by using LUBE prediction intervals model, the improved LUBE prediction intervals model and the LUBE prediction intervals model based on the improved PSO algorithm. The experimental results showed that the prediction intervals coverage probability (PICP) of the LUBE prediction intervals model, the improved LUBE prediction intervals model and the LUBE prediction intervals model based on the improved PSO algorithm were 75%, 80% and 80%, respectively, and the prediction intervals normalized average width (PINAW) were 0.3938, 0.3725 and 0.3629, respectively. It can be concluded that the LUBE prediction intervals model based on the improved PSO algorithm has higher prediction accuracy and better fitting ability.
Deep learning is an important branch of neural networks, which has high accuracy in classification and regression problems, and has been widely used. However, its performance is greatly affected by the parameters. In ...
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ISBN:
(数字)9781665470452
ISBN:
(纸本)9781665470469;9781665470452
Deep learning is an important branch of neural networks, which has high accuracy in classification and regression problems, and has been widely used. However, its performance is greatly affected by the parameters. In this paper, an improved particleswarmalgorithm named as PSO-C is proposed to automatically train the parameters of the feedforward neural networks. In the proposed algorithm, the curiosity factor is introduced to divide the particles into two categories with different curiosity characteristics so as to improve the exploration ability and information mining ability of the particleswarms. At the same time, a chaotic factor is also introduced to avoid the local optimum problem during the neural network's training. The simulation results show that the PSO-C has better optimization effect on the whole.
A particleswarmoptimization (TVPSO) algorithm with time varying parameters is proposed to improve the performance of particleswarmoptimization (PSO) algorithm by two improvements. Aiming at the fact general PSO al...
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ISBN:
(纸本)9781538612446
A particleswarmoptimization (TVPSO) algorithm with time varying parameters is proposed to improve the performance of particleswarmoptimization (PSO) algorithm by two improvements. Aiming at the fact general PSO algorithms have the disadvantages of falling into local optima caused by linearly decreased inertia weight. TVPSO uses the related properties of the trigonometric function to improve the dynamic changes of inertia weight along With Time. The inertia weight maintains a large value in the initial stage, and decreases gradually and reaches a small value at the end. Thus, the global search capability and convergence performance were improved;In order to cope with changes in inertia weight, learning factors also change with time. TVPSO and the other latest particle swarm optimization algorithms are tested on 10 functions at the same time. Experimental results show that TPSO has faster search speed and stronger global search capabilities.
in this paper,through the research of the existing particle swarm optimization algorithm and its improved algorithm,a particle swarm optimization algorithm improvement program is proposed,and the experimental results ...
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in this paper,through the research of the existing particle swarm optimization algorithm and its improved algorithm,a particle swarm optimization algorithm improvement program is proposed,and the experimental results show that this improved algorithm not only does not increase the complexity,but also has greater improvement in the convergence speed and stability comparing with the original algorithm.
In order to cope with the increase in carbon emissions caused by the excessive use of fossil energy, the world's energy structure needs to be adjusted. Distributed photovoltaic (PV) hydrogen production system, as ...
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ISBN:
(纸本)9798350377477;9798350377460
In order to cope with the increase in carbon emissions caused by the excessive use of fossil energy, the world's energy structure needs to be adjusted. Distributed photovoltaic (PV) hydrogen production system, as an integrated energy system, is an important pathway to the zero-carbon goal. But distributed PV is prone to face the shading situation, while the PV module power output curve will show the phenomenon of multiple peaks. The traditional maximum power tracking methods are prone to fall into the local optimal situation, and the intelligent tracking methods are inherently more complex. The hydrogen production electrolyzer puts forward the requirement of stability on the output power of the PV power supply. Existing research focuses on the maximum power tracking of PV system, or the control of electrolyzer, lacking the coupled modeling control research of distributed PV hydrogen production system. In order to fill the gap of the existing research, this paper proposes a composite algorithm which combines the Improved particleswarmalgorithm (IPSO) with the Improved Perturbation Observation (IP&O) method. The initial population and termination conditions of the particleswarm search method (PSO) are improved to enhance the convergence speed of the algorithm and reduce the oscillations during the convergence process of the algorithm, and the IP&O method is utilized to provide stable inputs for the electrolytic tanks. The performance of the algorithm is verified through the comparative experiments with other algorithms.
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