The sample data for reinforcement learning algorithms often exhibit sparsity and instability, making the training results susceptible to falling into local optima. Mini-Max-Multi-agent Deep Deterministic Policy Gradie...
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The sample data for reinforcement learning algorithms often exhibit sparsity and instability, making the training results susceptible to falling into local optima. Mini-Max-Multi-agent Deep Deterministic Policy Gradient (M3DDPG) algorithm is a multi-agent reinforcement learning algorithm, which introduces the minimax theorem into Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. It also has unstable convergence caused by sparse sample data and randomization. However, the particleswarm Optimisation (PSO) algorithm, unlike traditional reinforcement learning methods, involves the construction of independent populations of policy networks to generate sample data, followed by training the reinforcement learning algorithm. PSO optimizes and updates the policy population based on a fitness function, aiming to enhance the efficiency and convergence speed of the algorithm in learning from the sample data. In order to address the multi-agent pursuit-evasion problem, we propose the PSO-M3DDPG algorithm, which combines the PSO algorithm with the M3DDPG algorithm. Through experimental simulations, the improved algorithm demonstrates superior training results and faster convergence speeds, thus validating its effectiveness.
The distribution of ground thermal conductivity (As) is a significant consideration for the design of ground source heat pump systems. This paper proposed a new method to estimate the layered As for U-tube borehole he...
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The distribution of ground thermal conductivity (As) is a significant consideration for the design of ground source heat pump systems. This paper proposed a new method to estimate the layered As for U-tube borehole heat exchanger (BHE) using particleswarmoptimization (PSO) and distributed thermal response test (DTRT) data. Firstly, a quasi-3D heat transfer model considering geothermal gradient for U-tube BHE had been built to simulate fluid temperature profiles. And the experiment in DTRT was performed using a 45 m sandbox. Then, the layered As were estimated through PSO and DTRT data based on the established model. The results showed that the convergent As distribution could be reliably obtained by short-term DTRT. Furthermore, the estimation results are independent of the number of layers and data points. The mean difference in As distribution between 9 layers and 45 layers is 0.007 W/(m & BULL;K) and the layered As also could be obtained when the number of layers is more than the number of temperature data points, and the root mean square error between 30 and 220 points is less than 0.3 W/(m & BULL;K).
The proliferation of renewable energy sources within distribution systems has given rise to a new structure known as microgrids. These microgrids are small power grids comprised of both controllable and uncontrollable...
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The proliferation of renewable energy sources within distribution systems has given rise to a new structure known as microgrids. These microgrids are small power grids comprised of both controllable and uncontrollable loads. In the distribution system, microgrids can use renewable energy sources to be operated in far-away regions at lower investment costs. The energy industry faces numerous problems, including problems with energy efficiency, ensuring system confidence, and reducing the destructive environmental effects. Load management programs can help address the challenges confronting the energy industry. This essay proposes a method for evaluating the load responsiveness in microgrids. A combined algorithm, including a gravitational search algorithm as well as particleswarmoptimization, to address a multi-objective optimization function. The Latin hypercube sampling method is adopted to create diverse scenarios, which are reduced by the K-means method. The objective function includes grid losses, generation costs, confidence index, and voltage stability. The suggested method is implemented on a modified 69-bus system consisting of wind turbines, solar power stations, and energy storage systems using the combined optimizationalgorithm in Matlab. The results show that increasing renewable energy production reduces power losses, power consumption, and cost per kilo and improves voltage deviation. Adding renewables and energy storage also reduces power fluctuations and grid losses. Adding renewable sources brings benefits such as reduced production costs, losses, and improved voltage profiles. From the simulation outcomes, it can be seen that the average voltage profile has been improved by about 4%. Also, increasing the responsive load has improved the mains voltage profile and increased by about 10%.
This study focuses on existing ventilation, heating, and air conditioning systems in public buildings and mainly considers three objectives: system energy consumption, indoor thermal comfort, and efficiency. A multi-o...
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This study focuses on existing ventilation, heating, and air conditioning systems in public buildings and mainly considers three objectives: system energy consumption, indoor thermal comfort, and efficiency. A multi-objective diagnosis system is established, which correlates evaluation indicators with key equipment, forming a clear hierarchical diagnosis system. Meanwhile, particle swarm optimization algorithm is combined with linear weighting method to optimize the operating parameters of key equipment based on the diagnosis results, obtaining the optimal parameters of various operating scenes. For winter conditions, the average system energy consumption is 34.2 W/m2, the average system energy efficiency ratio is 1.5, and the average indicator for indoor thermal environment is 1.06. For summer conditions, the average energy consumption of the systems is 28.9 W/m2, the average energy efficiency ratio is 2.2, and the average indicator for indoor thermal environment is - 0.82. Compared with the measured results, most of the optimized indicators are better, but the system energy efficiency ratio is slightly lower than the measured results for winter conditions. Through the established diagnosis system and optimization method, this research evaluates and optimizes the existing the systems in public buildings. Demonstrating the effectiveness of the established diagnosis system and optimization method.
Vibration displacement is one of the key parameters in fault diagnosis of vibrating screens. Monitoring of acceleration signals of vibrating screens can be disturbed due to various factors such as on-site working cond...
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Vibration displacement is one of the key parameters in fault diagnosis of vibrating screens. Monitoring of acceleration signals of vibrating screens can be disturbed due to various factors such as on-site working conditions and equipment. In order to obtain accurate displacement signals of vibrating screen, the method for converting vibration acceleration to displacement based on improved Savitzky-Golay (S-G) filter is proposed. The particleswarmoptimization (PSO) algorithm is used to optimize the window length of the S-G filter with the fixed polynomial. The filters are cascaded to denoise the signals multiple times. The reasonable regularization parameter of the Smoothed Prior Approach (SPA) is calculated to remove the trend item from the acceleration signals. The vibration displacement is obtained by integrating the preprocessed acceleration data in the frequency domain. The results demonstrate that the objectivity of parameter selection of filter is improved, and the denoising effect is significant. The filtering effect of the filter is further improved after cascading. It becomes better as the number of stages of cascade increases. The vibration displacement can be obtained accurately by the proposed method. The vibration test platform is built to verify the correctness of the method.
Accurately aligning the same users on different flat social networks to merge user information and create more nuanced user profiles is critical. However, the current research in this area faces challenges related to ...
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Accurately aligning the same users on different flat social networks to merge user information and create more nuanced user profiles is critical. However, the current research in this area faces challenges related to low efficiency and inadequate alignment accuracy. To address these challenges, we introduce a cross-social network user alignment model based on multi-dimensional user features (MDUF). First, inspired by the principles of entity recognition and Hartley's association method, we employed a block matrix association algorithm to project the original dataset into different high-dimensional spaces. Second, we proposed a new inertia weight calculation method to improve the convergence speed from linear to nonlinear transformations. This method improves the performance of traditional particle swarm optimization algorithms. Finally, we utilize improved particleswarmoptimization and residual connection techniques to optimize bidirectional long short-term memory networks. The experimental results show that our proposed model significantly outperforms traditional alignment models in terms of alignment efficiency and accuracy, which is highly practical and has the potential to inspire further research on social network user alignment.
The separation of the Mo-92 and Mo-100 isotopes from the natural composition to the 99.99% enrichment level in a three-section squared-off cascade with 200 centrifuges was investigated. The parameters of this cascade ...
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The separation of the Mo-92 and Mo-100 isotopes from the natural composition to the 99.99% enrichment level in a three-section squared-off cascade with 200 centrifuges was investigated. The parameters of this cascade were optimized using the 3SQC-PSO and 3SQC-GOA codes and the PSO and GO algorithms. The objective function of the indicated optimization codes is to simultaneously maximize the cascade capacity, the recovery factor, and the D-function. The results obtained show that the separation of the Mo-92 and Mo-100 isotopes should be done in two and one separation steps, respectively. A single cascade of most suitable configuration for the separation of both the Mo-92 and Mo-100 isotopes is proposed. The recovery separations of the Mo-92 and Mo-100 isotopes in this cascade comprise 92 and 95%, respectively.
Photoelectric encoders are widely used in high-precision measurement fields such as industry and aerospace because of their high precision and reliability. In order to improve the subdivision accuracy of moire grating...
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Photoelectric encoders are widely used in high-precision measurement fields such as industry and aerospace because of their high precision and reliability. In order to improve the subdivision accuracy of moire grating signals, a particleswarmoptimization compensation model for grating the subdivision error of a photoelectric encoder based on parallel iteration is proposed. In the paper, an adaptive subdivision method of a particleswarm search domain based on the honeycomb structure is proposed, and a raster signal subdivision error compensation model based on the multi-swarmparticle swarm optimization algorithm based on parallel iteration is established. The optimizationalgorithm can effectively improve the convergence speed and system accuracy of traditional particleswarmoptimization. Finally, according to the subdivision error compensation algorithm, the subdivision error of the grating system caused by the sinusoidal error in the system is quickly corrected by taking advantage of the high-speed parallel processing of the FPGA pipeline architecture. The design experiment uses a 25-bit photoelectric encoder to verify the subdivision error algorithm. The experimental results show that the actual dynamic subdivision error can be reduced to 1/2 before compensation, and the static subdivision error can be reduced from 1.264 '' to 0.487 '' before detection.
In order to achieve maximum carbon reduction during the operation of pure electric buses, the author proposes a re estimation of carbon emissions in international trade based on evolutionary algorithm analysis of elec...
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In order to achieve maximum carbon reduction during the operation of pure electric buses, the author proposes a re estimation of carbon emissions in international trade based on evolutionary algorithm analysis of electric vehicle green energy regeneration systems. On the basis of analyzing single line scheduling, the author focuses on studying the regional scheduling mode of pure electric buses, and establishes a multi-objective optimization model for pure electric bus regional scheduling considering carbon benefits with the goals of minimizing the number of operating vehicles, minimizing the empty distance, and maximizing carbon benefits. Based on the actual operation data of pure electric buses 146 and 149 in a certain city, the author used an improved particle swarm optimization algorithm to solve the regional scheduling problem of pure electric buses according to the characteristics of the model. The results indicate that assuming other conditions remain unchanged, when the price of diesel rises to around 7.9 yuan, its operating costs will exceed those of pure electric buses, and the cost advantage of diesel vehicles will gradually decrease. Other conditions remain unchanged, and when the battery price per vehicle drops to around 300,000 yuan, the operating cost of pure electric buses will be lower than that of diesel vehicles. Conclusion: Under the premise of considering carbon benefits, adopting regional dispatch mode for pure electric buses has better economic efficiency and is more conducive to the promotion of pure electric buses.
作者：
Zhang, XiaYang, YueXiangnan Univ
Coll Comp & Artificial Intelligence Chenzhou 423000 Hunan Peoples R China Cent South Univ
Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China
PID controller parameter optimization is crucial for improving system stability, reliability, and responsiveness. To improve the efficiency of a PID controller, a hybrid algorithm, HPSO, based on particleswarm optimi...
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PID controller parameter optimization is crucial for improving system stability, reliability, and responsiveness. To improve the efficiency of a PID controller, a hybrid algorithm, HPSO, based on particle swarm optimization algorithm (PSO) and simulated annealing (SA) algorithms is proposed. The HPSO algorithm is used to optimize the control parameters. Adaptive weights and dynamic learning factors are introduced to improve the global optimization ability of the PSO algorithm. The simulated annealing (SA) mechanism is incorporated into the particleswarmoptimization (PSO) algorithm. The Metropolis criterion and cooling mechanism are used to guide the population to accept inferior solutions with dynamic jump probabilities, so that the algorithm can escape local optima and effectively improve its global search ability. The experimental results show that compared with the SA algorithm, basic particle swarm optimization algorithm and inertial weight coefficient particle swarm optimization algorithm, the HPSO algorithm has fast convergence speed and strong global search ability. Then, the population particles are defined using the coefficients to implement a unique fitness function, which is consistent with the control objectives of achieving fast system response while minimizing overshoot. Finally, taking a typical second-order temperature control delay system and CNC machine feed servo system as the control objects, the HPSO algorithm is compared with genetic algorithm and seeker optimizationalgorithm. The simulation results show that the optimized PID parameters obtained from the HPSO algorithm has low overshoot, better steady-state, and dynamic response.
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