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.
Local defects in components are an important factor responsible for causing damage to steel structures. Hence, metal magnetic memory (MMM) has been used to investigate this issue in recent years. MMM can detect defect...
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Local defects in components are an important factor responsible for causing damage to steel structures. Hence, metal magnetic memory (MMM) has been used to investigate this issue in recent years. MMM can detect defects based on variations in the self-magnetic leakage of ferromagnetic materials. However, there have been problems of difficulty in quantifying the theoretical parameters of the magnetic charge model and the single character-ization of magnetic feature parameters, when using MMM for quantitative analysis of defects. Therefore, the particleswarmoptimization (PSO) algorithm was introduced to quicky and accurately quantify the parameters of the magnetic charge model. Theoretical values were compared with experimental data to verify the accuracy of the proposed method. Then, the defect information (defect width and defect depth) was changed and the variation patterns of the magnetic signal and characteristic parameters were analyzed. Finally, the weight of each characteristic parameter was calculated using the entropy value method. A theoretical formula to comprehen-sively describe defects using multi-feature parameters was proposed. The results show that the theoretical values calculated based on the parameter identification of the PSO algorithm well agreed with the experimental data. Moreover, the proposed formula well described the relationship between the defect width and characteristic parameters. This study is expected to provide a basis for improving the quantitative analysis of MMM.
In view of the manipulator system is a highly coupled, nonlinear dynamic characteristics and the system structure and parameters, such as there are many unpredictable factors in the practical work of multiple input mu...
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ISBN:
(纸本)9781450384162
In view of the manipulator system is a highly coupled, nonlinear dynamic characteristics and the system structure and parameters, such as there are many unpredictable factors in the practical work of multiple input multiple output system, designed a fuzzy neural network controller, and combined with particle swarm optimization algorithm for fuzzy neural network controller parameter setting. Through MATLAB simulation, it is proved that the scheme has strong robustness and stability for the control system, and effectively solves the trajectory tracking problem of manipulator.
Analog design can be considered as a multidimensional optimization problem since it involves trade-offs between several circuit parameters. Various optimization techniques have been proposed to reduce the cycle time o...
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ISBN:
(纸本)9781728192017
Analog design can be considered as a multidimensional optimization problem since it involves trade-offs between several circuit parameters. Various optimization techniques have been proposed to reduce the cycle time of analog design. We propose a hybrid particle swarm optimization algorithm with linearly decreasing inertia weight for the optimization of analog circuit design. The proposed method is validated in a differential amplifier circuit with a current mirror load. Promising simulation results demonstrate that the proposed method can significantly reduce the design time required for analog circuits.
To solve the nonlinear constrained optimization problem, a particle swarm optimization algorithm based on the improved Deb criterion (CPSO) is proposed. Based on the Deb criterion, the algorithm retains the informatio...
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To solve the nonlinear constrained optimization problem, a particle swarm optimization algorithm based on the improved Deb criterion (CPSO) is proposed. Based on the Deb criterion, the algorithm retains the information of 'excellent' infeasible solutions. The algorithm uses this information to escape from the local best solution and quickly converge to the global best solution. Additionally, to further improve the global search ability of the algorithm, the DE strategy is used to optimize the personal best position of the particle, which speeds up the convergence speed of the algorithm. The performance of our method was tested on 24 benchmark problems from IEEE CEC2006 and three real-world constraint optimization problems from CEC2020. The simulation results show that the CPSO algorithm is effective.
Concerning the issue of low energy recovery efficiency of compound braking in Battery electric vehicles, an electromechanical composite braking control strategy based on road recognition and particleswarm optimizatio...
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Concerning the issue of low energy recovery efficiency of compound braking in Battery electric vehicles, an electromechanical composite braking control strategy based on road recognition and particle swarm optimization algorithm is proposed, which is different from the braking force control strategy of front and rear wheels with fixed ratio distribution before optimization. By analyzing the structure and working principle of the compound braking system of electric vehicle, a road surface identifier based on fuzzy algorithm is designed to track the peak adhesion coefficient of the road surface to obtain the maximum braking force of the brake. In addition, particleswarmoptimization (PSO) is used to modify the motor braking force, so as to maximize the efficiency of energy recovery, and CRUISE and MATLAB are used in simulation environment to carry out joint simulation analysis. The results show that compared with the control strategy before optimization, the proposed control strategy not only ensures the vehicle braking stability but also has shorter braking distance, shorter braking time, larger motor braking torque, and slower decrease of battery State of Charge (SOC) value. Under the cycle condition of New European Driving Cycle (NEDC), Federal Test Procedure (FTP) and Extra Urban Driving Cycle (EUDC), the State of Charge of the battery increased by 1.98%, 2.1%, and 0.5%, respectively.
Generally, the optimization of injection and production parameters in oilfields are carried out by using reservoir engineering method or mathematical algorithm individually, which limits the optimization efficiency an...
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Generally, the optimization of injection and production parameters in oilfields are carried out by using reservoir engineering method or mathematical algorithm individually, which limits the optimization efficiency and accuracy. To deal with this problem, the paper tries to improve production optimization performance by introducing reservoir engineering method into conventional particleswarmoptimization (PSO) in three ways: the preprocessing result by reservoir engineering method is used respectively as population initialization, the search space constraint and the particle velocity guide item in PSO. Results show that all the three improved optimization methods can speed up the convergence rate of PSO algorithm while keeping similar convergence results at the same time. Furthermore, the use of the reservoir engineering preprocessing scheme as the search space constraint obtains the best convergence performance and reduces the iteration calculations by 24.14%, providing an effective way to reduce calculation cost for reservoir production optimization in commercial oilfields.
The optimization of injection-production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this pr...
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The optimization of injection-production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this problem, this paper optimizes injection-production parameters by combining an improved particle swarm optimization algorithm to study the relationship between injection-production parameters and the net present value. In the process of injection-production parameter optimization, the particle swarm optimization algorithm has shortcomings, such as being prone to fall into local extreme points and slow in convergence speed. Curve adaptive and simulated annealing particle swarm optimization algorithms are proposed to further improve the optimization ability of the particle swarm optimization algorithm. Taking the Tarim oil field as an example, in different stages, the production time, injection volume and flowing bottom hole pressure were used as input variables, and the optimal net present value was taken as the goal. The injection-production parameters were optimized by improving the particle swarm optimization algorithm. Compared with the particleswarmalgorithm, the net present value of the improved scheme was increased by about 3.3%.
Classification of spatial exploration data for exploration targeting using neuro-fuzzy models means that the many spatial values have to be simplified and assigned to a few classes. The simplification of complex geolo...
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Classification of spatial exploration data for exploration targeting using neuro-fuzzy models means that the many spatial values have to be simplified and assigned to a few classes. The simplification of complex geological information, which illustrates a high degree of variability, results in overly simplistic models based on the presumption of homogeneous earth. However, such an assumption is not valid. In this paper, we illustrate the superiority of using continuously weighted spatial evidence values compared to discretely weighted evidence data, and how continuously weighted spatial evidence values can increase the efficiency of neuro-fuzzy exploration targeting models. The results of this study demonstrate that neuro-fuzzy targeting model generated with continuously weighted spatial evidence values is superior to that of the neuro-fuzzy model generated with discretely weighted exploration evidence data.
In this study, an optimization design method based on adaptive neuro-fuzzy inference system (ANFIS) and modified particleswarmoptimization (PSO) algorithm is proposed to enhance the performance of high-frequency ult...
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In this study, an optimization design method based on adaptive neuro-fuzzy inference system (ANFIS) and modified particleswarmoptimization (PSO) algorithm is proposed to enhance the performance of high-frequency ultrasonic transducers (UTs). The optimization indexes include center frequency (CF) and -6 dB bandwidth (BW), and the design parameters (DPs) include thickness (T-p) and size (S-p) of piezo-electric layer, thicknesses of matching layers (Ag-epoxy and Parylene C). Based on simulated results, an ANFIS model is established to characterize the effect of DPs on the performance indexes (PIs). optimization targets are set as CF of 30 MHz and BW of 65%, the modified PSO algorithm is adopted to optimize the design parameters of high-frequency UT. The optimized T-p and S-p are 110 mu m and 5.2 mm. The optimized thicknesses of Ag-epoxy (T-AE) and Parylene C (T-pc) are 16 mu m and 20 mu m, respectively. Simulations and experiments of high-frequency UT are conducted based on the optimized DPs, the results well agree with the designed ones. The CF and the BW of UT fabricated by the developed method are 26.77(+/- 0.02) MHz and 65.11(+/- 0.06) %, while the CF and the BW of UT fabricated by the traditional method are 23.42 MHz and 51.4%. (C) 2021 Elsevier Ltd. All rights reserved.
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