Searching an optimal value of the neural emulator adaptive rate presents a great problem. Indeed, a new scheme of neural emulators based on the Particle Swarm Optimization (pso) algorithm for nonlinear systems is adop...
详细信息
Searching an optimal value of the neural emulator adaptive rate presents a great problem. Indeed, a new scheme of neural emulators based on the Particle Swarm Optimization (pso) algorithm for nonlinear systems is adopted in this paper. The main goal of this approach consists in adjusting effectively the neural emulator adaptive rate in order to accelerate the convergence speed and to improve the precision degree. The obtained results are compared with those reached with an intelligent tuning strategy. An experimental validation of the new emulator adaptation is carried on chemical reactor. Efficiency of the proposed method is proved according to the obtained performances.
This work enhanced the laser output power from a nanolaser device utilizing an InGaAsP waveguide. We added the DBR structure to the meta-waveguide gain strips with 14 waveguides. Then, we used the particle swarm optim...
详细信息
This work enhanced the laser output power from a nanolaser device utilizing an InGaAsP waveguide. We added the DBR structure to the meta-waveguide gain strips with 14 waveguides. Then, we used the particle swarm optimization (pso) algorithm to improve the dimensions, especially the meta-WG's width and the DBR structure's x, y, span, and period dimensions. From this new design, we obtained an increase in the output laser power in both the backward and forward directions, respectively, as the value of the increase in the backward direction reached ten orders of magnitude, meaning an output power of 2 mW. In comparison, it increased the laser output power in the forward direction by 2.61 orders of magnitude, indicating an output power value of 0.7 mW. This increase was achieved while maintaining the original device dimensions and operating at room temperature.
As the major power consumer in buildings, Heating, ventilation and air conditioning (HVAC) systems can significantly contribute to the economical operation of the power system during the peak periods in summer or wint...
详细信息
As the major power consumer in buildings, Heating, ventilation and air conditioning (HVAC) systems can significantly contribute to the economical operation of the power system during the peak periods in summer or winter, by flexibly responding to the grid demands. Indoor air temperature set-point resetting is a typical passive demand response (DR) control strategy that have potentials to be widely implemented by inverter air conditioners in residential buildings. However, in the existing small and medium-size public buildings, the compressor motor of HVAC systems mostly does not allow for stepless speed changes. This means that the indoor temperature set-point resetting strategy may results in frequent on/off switching of the chiller plant, leading to a compromised lifespan. Although rooftop photovoltaic systems (PVs) are expected to be widely adopted in new buildings, their potential for peak load shaving or valley load filling has received limited investigation. This study proposes a grey-box model-based demand side management (DSM) method for variable air volume systems (VAVs) and rooftop PVs in public buildings. The DSM method utilizes the particle swarm optimization (pso) algorithm to determine optimal indoor air temperature and/or evaporator outlet water temperature set-point schedules for the VAVs, as well as optimal charging/discharging schedules for the storage battery of rooftop PVs. This approach aims to reduce the peak power demands of the integrated system without causing severe thermal discomfort, increasing chiller mechanical costs (refers to frequent on/off switching of the chiller plant in this paper), or overcharging/over-discharging of storage batteries. The results of case studies demonstrate that the strategy of co-resetting indoor air temperature - evaporator outlet water temperature set-points outperforms the indoor air temperature set-point resetting strategy in terms of both peak power demands reduction and mechanical cost saving when applie
This study proposes a hybrid pso-EDO algorithm, integrating Particle Swarm Optimization (pso) and the Exponential Distribution Optimizer (EDO) for efficient and accurate estimation of soil property parameters. The pro...
详细信息
This study proposes a hybrid pso-EDO algorithm, integrating Particle Swarm Optimization (pso) and the Exponential Distribution Optimizer (EDO) for efficient and accurate estimation of soil property parameters. The proposed algorithm combines the strengths of Standard pso (Spso) and the Exponential Distribution Optimizer (EDO). Three key innovations are introduced: (1) SPM chaotic mapping enhances initial population diversity;(2) dynamic inertia weight balances global exploration and local exploitation;(3) the memoryless property of EDO improves escape capability from local optima. Benchmark tests demonstrate that pso-EDO achieves near-theoretical optimal convergence errors (mean error <= 10-16 for unimodal functions such as F1 and F2) and reduces the computation time by 14.5% compared to EDO. For multimodal functions (e.g., F3), pso-EDO significantly outperforms pso-WOA (Particle Swarm Optimization-Whale Optimization algorithm) with a 22.3% reduction in error. Simulation experiments further validate its engineering practicality: in soil parameter estimation, pso-EDO completes 1000 iterations in just 1.95 s, with key parameters (e.g., sinkage coefficient n) controlled within a 7.32% error margin. This provides an efficient solution for real-time traversability assessment of autonomous vehicles on soft terrains.
A cutting-edge construction method called fitted construction allows for several parallel lines of work to speed up construction and enhance building quality. However, achieving optimal project decisions for global co...
详细信息
A cutting-edge construction method called fitted construction allows for several parallel lines of work to speed up construction and enhance building quality. However, achieving optimal project decisions for global construction projects demands a high level of objective decision-making. To enhance the decision- making process, this research utilizes particle swarm algorithms to optimize construction project decisions in assembled buildings. To tackle the issue of early convergence in particle swarm algorithms, three swarm enhanced particle swarm algorithms are proposed by merging the variational mechanism of the differential evolution algorithm and quantifying the decision making tasks for assembly building construction projects to be solved by the enhanced particle swarm algorithm. Regarding the research results, the upgraded particle swarm algorithm achieved a fundamental convergence in 20 iterations whilst resolving the Sphere, Rosebrock, Rastrigin, and Griewank functions. The improved particle swarm algorithm converges to an optimal solution of -19.208 within 20 iterations on the Holder function, with an optimal domain of [8.055, -9.665]. The results of the optimization study for the decision-making problem of the assembly building project demonstrate that implementing Sigmoid smoothing yields a minimum duration problem of 0.755 and a minimum duration of 45 days. The optimal cost and time required to solve the problem of economic maximisation strategy using the enhanced particle swarm method are 500,000 and 52 days, respectively. The results indicate that the improved particle swarm approach outperforms conventional algorithms in the decision-making process for assembly building projects, maintaining computational accuracy throughout.
In this paper, a new dual-band Wilkinson power divider (WPD) is designed and fabricated using novel low and high impedance stubs instead of quarter-wavelength transmission lines. The proposed circuit was analyzed usin...
详细信息
In this paper, a new dual-band Wilkinson power divider (WPD) is designed and fabricated using novel low and high impedance stubs instead of quarter-wavelength transmission lines. The proposed circuit was analyzed using odd and even mode analysis, and the optimal values of design parameters were obtained using the particle swarm optimization algorithm. The designed power divider has input reflection coefficients (S-11) of -22.1 and -17 dB at the first operating frequency of 2.2 GHz and the second operating frequency of 14.2 GHz, respectively. It also improves stop-band and fractional bandwidth (FBW) while maintaining a simple topology. The proposed WPD suppresses undesired harmonics from the second to the fifth with an attenuation level of less than -20 dB in the first band and generates a broad stop-band (4.4-11.5 GHz). In the first band, the FBW is 54.5%, and in the second band, it is 20.1%.
This article presents a novel automatic pso-based back analysis method for mechanical parameters of block discrete element method. Incorporating particle swarm optimization (pso) algorithm and recursive algorithm, a p...
详细信息
This article presents a novel automatic pso-based back analysis method for mechanical parameters of block discrete element method. Incorporating particle swarm optimization (pso) algorithm and recursive algorithm, a parameter back-analysis program is developed. Two cases, a laboratory test of columnar jointed rock mass and a typical landslide, are implemented for validation. The results show that the proposed method is feasible and accurate. As an automatic solution for parameter back analysis, this work has broad application prospects in geotechnical engineering.
An improved particle swarm optimization (pso) algorithm is presented to optimize the texture shape of the sealing ring in order to enhance the load carrying capacity (LCC). The shape optimization uses the unrelated ge...
详细信息
An improved particle swarm optimization (pso) algorithm is presented to optimize the texture shape of the sealing ring in order to enhance the load carrying capacity (LCC). The shape optimization uses the unrelated generation method of the texture curves, and two splines are applied to fit the boundary of the texture to improve the continuity. The inertia factor is adjusted through the differential strategy to enhance the local optimization capacity. The optimization model is verified by two optimization cases. The optimized texture is bullet-shaped. The increase of inlet oil pressure transforms the optimized texture from the bullet shape to the inner spiral shape. In addition, the increase of the texture depth leads to the gradual elongation of the texture shape. The increase in the number of textures flattens the seal texture. The results shows that the improved pso algorithm has high global optimization ability of the texture shape for the sealing ring in the vehicle transmission through appropriate parameter settings.
In this paper, an enhanced FCS-MPC control scheme is introduced for DFIG-based wind turbines. Controllers employing FCS-MPC to govern power converters often exhibit elevated switching frequencies, leading to notable s...
详细信息
ISBN:
(纸本)9798350351088;9798350351095
In this paper, an enhanced FCS-MPC control scheme is introduced for DFIG-based wind turbines. Controllers employing FCS-MPC to govern power converters often exhibit elevated switching frequencies, leading to notable switching losses and the potential for damaging power converters. This paper addresses this issue by using the pso meta-heuristic algorithm to optimize switching frequencies. Notably, the optimization is performed while adhering to the IEEE Standards' waveform requirements for the current injected into the PCC. FCS-MPC with a single step prediction horizon performs well but leads to high switching frequencies for power converters. This issue persists when using a prediction horizon with dual step. To overcome this issue, a pso optimized penalty term is incorporated into the cost function to minimize the overall switching frequency. The obtained results demonstrates a significant reduction in the switching frequency of DFIG converters without compromising control. The average switching frequencies for RSC and GSC are decreased by 1,67 kHz and 14,98kHz, respectively, in comparison to conventional MPC. While the THD level of the grid current is evaluated at 3.98%, which is fully coplying with IEEE 519 Std.
The introduction of intelligent search algorithms provides an excellent solution to the global and local path planning problems of UAVs. The most classic and representative is Particle Swarm Optimization (pso). Howeve...
详细信息
ISBN:
(纸本)9798350386783;9798350386776
The introduction of intelligent search algorithms provides an excellent solution to the global and local path planning problems of UAVs. The most classic and representative is Particle Swarm Optimization (pso). However, when applying pso algorithm to UAV path planning, it is easy to be constrained by factors such as environmental changes and fail to achieve the expected results. In order to solve the communication delay and communication reliability problems in wireless communication, a path planning study under successive control of UAVs is proposed. In this study, the defects of pso algorithm and the problems in UAV path planning are improved. The improved algorithm introduces the adaptive weight into the update mechanism, and combines the Levy flight strategy to improve the global search strategy to reduce the impact of environmental changes on the algorithm update, so as to ensure the optimal solution. It can be seen from the simulation results that the improved pso algorithm has achieved significant improvement in data update accuracy, local and overall search capabilities. Compared with genetic algorithm, grey wolf optimization algorithm and pso algorithm, the improved pso algorithm has obvious advantages such as fast planning speed and short planning path.
暂无评论