Particle Swarm Optimization-based co-state initialization for low-thrust minimum-fuel trajectory optimization

Hecht, Grant R.; Botta, Eleonora M.

doi:10.1016/j.actaastro.2023.06.021

Cited by 11 publications

(2 citation statements)

References 29 publications

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“…In dynamic LSSVM, the penalty parameter and kernel parameter are determined by Cauchy Particle swarm optimization. Particle swarm optimization is a type of stochastic global optimization algorithm derived from the social behavior of birds flocking 19 , 20 . Update the position and velocity of each particle as follows: where is the inertia weight, is the best previous experience of individual particle, is the global best experience, and are the constants, and is the random number from 0 to 1.…”

Section: Optimized Dynamic Lssvmmentioning

confidence: 99%

Fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine

Xiong,

Han,

Zhang

2023

Sci Rep

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In order to ensure the normal operation of rotating equipment, it is very important to quickly and efficiently diagnose the faults of anti-friction bearings. Hereto, fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine (LSSVM) is presented in this paper. Bi-dimensional ensemble local mean decomposition, an extension of ensemble local mean decomposition from one-dimensional signal processing to Bi-dimensional signal processing, is used to extract the features of anti-friction bearings. Moreover, an optimized dynamic LSSVM is used to fault diagnosis of anti-friction bearings. The experimental results show that Bi-dimensional ensemble local mean decomposition is superior to Bi-dimensional local mean decomposition, optimized dynamic LSSVM is superior to traditional LSSVM, and the proposed Bi-dimensional ensemble local mean decomposition and optimized dynamic LSSVM method is effective for fault diagnosis of anti-friction bearings.

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Section: Optimized Dynamic Lssvmmentioning

confidence: 99%

Fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine

Xiong,

Han,

Zhang

2023

Sci Rep

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“…The use of microgrids signifies a significant change in how we manage issues pertaining to energy security, dependability, and the reduction of carbon emissions, as the global energy landscape continues to develop. [1][2][3][4][5] The emergence of microgrid designs is a result of their inherent benefits, such as improved energy dependability, heightened resilience in the face of disturbances, and less reliance on centralized power grids. Furthermore, the incorporation of sustainable energy sources is in line with the overarching objectives of addressing climate change and reducing greenhouse gas emissions.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

Vafaeva,

Raju,

Ballabh

et al. 2024

E3S Web Conf.

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This study investigates the optimization of the size of a solar-wind hybrid microgrid using Particle Swarm Optimization (PSO) to improve energy production efficiency, economic feasibility, and overall sustainability. By using past solar and wind resource data, load demand profiles, and system component specifications, the PSO algorithm effectively maximized the capabilities of solar panels and wind turbines. The findings indicate a significant rise in daily energy production, with a 15% enhancement in solar panel capability and a 12% boost in wind turbine capability. The increased energy production plays a crucial role in dealing with the natural irregularity of renewable resources, hence enhancing the resilience and self-reliance of the microgrid. The economic calculations demonstrate significant improvements in the economic feasibility of the microgrid designs. The Levelized Cost of Energy (LCOE) undergoes a significant 10% decrease, suggesting a more economically efficient energy generation. Moreover, the payback time for the original expenditure is reduced by 15%, indicating faster returns on investment. The economic improvements highlight the practical advantages of using PSO for microgrid size, in line with the goal of creating sustainable energy solutions while minimizing economic costs. The improved performance of Particle Swarm Optimization (PSO) is shown by a thorough comparison study with other optimization approaches, such as Genetic Algorithms (GA) and Simulated Annealing (SA). The superior convergence rate of PSO, together with a 15% enhancement in solution quality relative to GA and SA, underscores the efficiency and efficacy of PSO in traversing the complex solution space associated with microgrid size. PSO’s comparative advantage makes it an effective tool for tackling the intricacies of integrating renewable energy, highlighting its potential for extensive use in microgrid design and optimization. The sensitivity evaluations demonstrate that the solutions optimized by the PSO are resilient even when important parameters vary, thereby highlighting the stability and dependability of the approach. In addition to technical and economic factors, the study evaluates the environmental consequences and social aspects of the optimum microgrid designs. The land use efficiency has seen a 10% enhancement, demonstrating the optimum application of area for renewable energy infrastructure. In addition, there is a 7% improvement in community approval, which demonstrates the algorithm’s ability to effectively handle social aspects and promote a comprehensive and socially acceptable approach to renewable energy projects.

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Distributed Swarm Optimization for the Solution of Boundary Value Problems in Astrodynamics

Hecht,

Botta

2023

J Astronaut Sci

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Particle Swarm Optimization-based co-state initialization for low-thrust minimum-fuel trajectory optimization

Cited by 11 publications

References 29 publications

Fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine

Fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine

Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

Distributed Swarm Optimization for the Solution of Boundary Value Problems in Astrodynamics

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