In this paper, parameter of ADRC for spacecraft attitude maneuvering is optimizated. Nonlinear dynamics model of spacecraft attitude describes attitude motion. particle swarm optimization algorithm is used for paramet...
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ISBN:
(纸本)9781479949557
In this paper, parameter of ADRC for spacecraft attitude maneuvering is optimizated. Nonlinear dynamics model of spacecraft attitude describes attitude motion. particle swarm optimization algorithm is used for parameter optimization of ADRC. The controller index which describes attitude adjustment capacity of three axes is designed. The influence of controller parameter is quantifiable on the control performance. The selection of parameter based on traditonal experience is avoided. Simulation results show that: the particle swarm optimization algorithm for system updates through the position and velocity. The system can quickly converge to the global optimal solution, and the parameter of ADRC is optimized.
The thickness of multi-layer absorbing material is optimized to obtain lower electromagnetic reflection coefficient by using particleswarmoptimization (PSO) algorithm in this paper. Two examples are employed to vali...
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ISBN:
(纸本)9783037859087
The thickness of multi-layer absorbing material is optimized to obtain lower electromagnetic reflection coefficient by using particleswarmoptimization (PSO) algorithm in this paper. Two examples are employed to validate the excellent performance of PSO. The results show that the reflection coefficient of absorbing material is less than-20 dB over the bandwidth of 2GHz similar to 18GHz, less than -25 dB over the narrowband of 9 GHz similar to 11GHz, less than-30 dB during the bandwidth of 9.5 GHz similar to 10.5 GHz. It also shows that the minimum value approaches to -48 dB in a certain range.
Partner selection decisions are an important component of production and supply chain management. However, partner selection is a typical multi-criteria decision-making problem and many factors affect partner selectio...
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ISBN:
(纸本)9783038352884
Partner selection decisions are an important component of production and supply chain management. However, partner selection is a typical multi-criteria decision-making problem and many factors affect partner selection;it is hard to select the most appropriate partners in real circumstances, especially in agricultural product supply chain. In this study, a discrete particle swarm optimization algorithm is proposed to solve the problem. A numerical example for partner selection of agricultural product supply chain is given to illustrate the application of the proposed method and shows the proposed method is feasible and effective.
In this paper, a space-time simulation model based on particle swarm optimization algorithm for stadium evacuation is presented. In this new model, the fast evacuation, going with the crowd and the panic behaviors are...
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ISBN:
(纸本)9781479914883
In this paper, a space-time simulation model based on particle swarm optimization algorithm for stadium evacuation is presented. In this new model, the fast evacuation, going with the crowd and the panic behaviors are considered and the corresponding moving rules are defined. The model is applied to a stadium and simulations are carried out to analyze the space-time evacuation efficiency by different behaviors. The simulation results show that the behaviors of going with the crowd and panic will slow down the evacuation process while quickest evacuation psychology can accelerate the process, and panic is helpful to some extent. The setting of parameters is discussed to obtain best performance. The simulation results can offer effective suggestions for evacuees under emergency situation.
Niche is an important technique for multi-peak function optimization. When the particleswarmoptimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling...
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ISBN:
(纸本)9783037859537
Niche is an important technique for multi-peak function optimization. When the particleswarmoptimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, an improved PSO algorithm based on niche technique is brought forward. PSO algorithm utilizes properties of swarm behavior to solve optimization problems rapidly. Niche techniques have the ability to locate multiple solutions in multimodal domains. The improved PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the niche technique. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on multi-peak function optimization.
While many of the previous applications based on the Taguchi method only focus on single-response optimization in static system, dynamic multiresponse optimization has received only limited attentions. optimization of...
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ISBN:
(纸本)9781479953769
While many of the previous applications based on the Taguchi method only focus on single-response optimization in static system, dynamic multiresponse optimization has received only limited attentions. optimization of dynamic multiresponse aims at finding out a setting combination of input controllable factors that will result in optimal solutions for all response variables at each signal level. However, it is often difficult to find an optimal setting when multiple responses are simultaneously considered because of their contradiction among the requirements. Hence, a new robust design optimization procedure based on response surface methodology is proposed in the article. The polynomial models of system sensitivity and the error variance for each response are firstly fitted, and corresponding individual desirability functions based on their respective characteristic are defined. Then, goal programming approach is used to resolve multiresponse optimization problems. Because the problems are often multiobjective optimization problems and are often with multipeak distribution, multiconstraint and high nonlinearity, traditional gradient algorithms are easy to obtain local optimal solutions. So a modified particle swarm optimization algorithm is proposed to search global optimal solution. The example shows that the proposed approach can obtain more effectively solutions for dynamic multiresponse optimization problems.
Process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating were optimized based on particleswarmoptimization (PSO) algorithm. BP neural network was applied to compute fitness of PSO ...
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ISBN:
(纸本)9783038352709
Process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating were optimized based on particleswarmoptimization (PSO) algorithm. BP neural network was applied to compute fitness of PSO algorithm. A BP neural network model was built. Process parameters of coating were optimized based on PSO algorithm. The results shown that maximal bonding strength was 33.08MPa. Process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating were obtained. The results were superior to design of orthogonal optimization. It provided definite reference for selecting the best process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating.
In order to reduce the active network loss, increase the power quality and voltage static stability of power system, an index function of multi-objective reactive power optimization is established. Then, an improved a...
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ISBN:
(纸本)9783038350033
In order to reduce the active network loss, increase the power quality and voltage static stability of power system, an index function of multi-objective reactive power optimization is established. Then, an improved adaptive chaotic particle swarm optimization algorithm is proposed to solve the problem. Through the using of cubic chaotic mapping, the particle population is initialized to enhance the diversity of its value;In the optimization process, poor fitness particles are updated with chaos disturbance, and their inertia weight are adjusted dynamically with particle's fitness value so as to avoid local convergence. Simulation of IEEE 30 bus system shows that the proposed algorithm for reactive power optimization can avoid premature convergence effectively, and converge to optimal solution rapidly.
Supermarkets need to make replenishment decisions for the vegetable category of the day without knowing exactly what the specific individual items and the price of the stock are. Accurate market demand analysis is cru...
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In the Internet of Things (IoT) scenario, the integration with cloud-based solutions is of the utmost importance to address the shortcomings resulting from resource-constrained things that may fall short in terms of p...
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In the Internet of Things (IoT) scenario, the integration with cloud-based solutions is of the utmost importance to address the shortcomings resulting from resource-constrained things that may fall short in terms of processing, storing, and networking capabilities. Fog computing represents a more recent paradigm that leverages the wide-spread geographical distribution of the computing resources and extends the cloud computing paradigm to the edge of the network, thus mitigating the issues affecting latency-sensitive applications and enabling a new breed of applications and services. In this context, efficient and effective resource management is critical, also considering the resource limitations of local fog nodes with respect to centralized clouds. In this article, we present FPFTS, fog task scheduler that takes advantage of particleswarmoptimization and fuzzy theory, which leverages observations related to application loop delay and network utilization. We evaluate FPFTS using an IoT-based scenario simulated within iFogSim, by varying number of moving users, fog-device link bandwidth, and latency. Experimental results report that FPFTS compared with first-come first-served (respectively, delay-priority) allows to decrease delay-tolerant application loop delay by 85.79% (respectively, 86.36%), delay sensitive application loop delay by 87.11% (respectively, 86.61%), and network utilization by 80.37% (respectively, 82.09%), on average.
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