Trajectory Optimization of Hypersonic Periodic Cruise Using an Improved PSO Algorithm

Periodic cruise has the potential to improve the fuel-saving efficiency of hypersonic cruise vehicles but is difficult to optimize. In this paper, hypersonic periodic cruise trajectory is analyzed theoretically and optimized by an improved Particle Swarm Optimization algorithm. Firstly, through theoretical analysis, it is determined that the optimal throttle curve can be parameterized as a switching function. Considering the optimization direction of algorithm, a new penalty function for the constraints of periodic cruise is proposed. Then, PSO algorithm is improved and applied in periodic cruise trajectory optimization. Numerical results demonstrate that optimized periodic cruise trajectory costs less fuel compared with steady-state cruise trajectory, and without computing gradient information, the proposed method is also robust. Finally, the fuel-saving mechanism of periodic cruise is explored by comparing with steady-state cruise, which reveals that periodic cruise trajectory has higher impulse and lift-drag ratio, but lower mechanical energy loss rate.

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Trajectory Optimization of Aircraft Vertical Flight Profile Based on Particle Swarm Optimization Algorithm Considering Pollutant Emission Cost

2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) ◽

10.1109/icmcce51767.2020.00391 ◽

2020 ◽

Author(s):

Jun Zhang ◽

Meina Zhang

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Trajectory Optimization ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Pollutant Emission ◽

Swarm Optimization ◽

Flight Profile

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Friction and mechanical energy loss

The Physics Teacher ◽

10.1119/1.2339868 ◽

1978 ◽

Vol 16 (3) ◽

pp. 173-175

Author(s):

Kenneth Flowers

Keyword(s):

Energy Loss ◽

Mechanical Energy ◽

Mechanical Energy Loss

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The Optimization of Project Time-Cost Based on Particle Swarm Optimization Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.791-793.1423 ◽

2013 ◽

Vol 791-793 ◽

pp. 1423-1426

Author(s):

Hai Min Wei ◽

Rong Guang Liu

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm Optimization Algorithm ◽

Cost Optimization ◽

Particle Swarm ◽

Implementation Process ◽

Pso Algorithm ◽

Project Schedule ◽

Swarm Optimization ◽

Schedule Optimization ◽

Schedule Management

Project schedule management is the management to each stage of the degree of progress and project final deadline in the project implementation process. Its purpose is to ensure that the project can meet the time constraints under the premise of achieving its overall objectives.When the progress of schedule found deviation in the process of schedule management ,the progress of the plan which have be advanced previously need to adjust.This article mainly discussed to solve the following two questions:establish the schedule optimization model by using the method of linear;discuss the particle swarm optimization (PSO) algorithm and its parameters which have effect on the algorithm:Particle swarm optimization (PSO) algorithm is presented in the time limited project and the application of a cost optimization.

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Analysis of Heat, Mass, and Momentum Transfer in the Rain Zone of Counterflow Cooling Towers

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.2818537 ◽

1999 ◽

Vol 121 (4) ◽

pp. 751-755 ◽

Cited By ~ 12

Author(s):

E. de Villiers ◽

D. G. Kro¨ger

Keyword(s):

Momentum Transfer ◽

Cooling Tower ◽

Mechanical Energy ◽

Performance Evaluations ◽

Cooling Towers ◽

Droplet Deformation ◽

One Dimensional ◽

Mechanical Energy Loss ◽

Simplifying Assumptions ◽

Semi Empirical

The rate of heat, mass, and momentum transfer in the rain zone of three counterflow cooling tower geometries is analyzed using simplifying assumptions and numerical integration. The objective of the analysis is to generate equations for use in a one-dimensional mathematical cooling tower performance evaluations. Droplet deformation is taken into account and momentum transfer is calculated from the air flow’s mechanical energy loss, caused by air-droplet interaction. A comparison of dimensionless semi-empirical equations and experimental data demonstrates the method’s capability to predict the pressure drop in a counterflow rain zone.

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Analysis of FBG Reflection Spectrum with PSO Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.182-183.1953 ◽

2012 ◽

Vol 182-183 ◽

pp. 1953-1957

Author(s):

Zhao Xia Wu ◽

Shu Qiang Chen ◽

Jun Wei Wang ◽

Li Fu Wang

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Pso Algorithm ◽

High Precision Measurement ◽

Swarm Optimization ◽

Fbg Sensor ◽

Fast Convergence Rate ◽

Force Sense

When the parameters were measured by using fiber Bragg grating (FBG) in practice, there were some parameters hard to measure, which would influenced the reflective spectral of FBG severely, and make the characteristic information harder to be extracted. Therefore, particle swarm optimization algorithm was proposed in analyzing the uniform force reflective spectral of FBG. Based on the uniform force sense theory of FBG and particle swarm optimization algorithm, the objective function were established, meanwhile the experiment and simulation were constructed. And the characteristic information in reflective spectrum of FBG was extracted. By using particle swarm optimization algorithm, experimental data showed that particle swarm optimization algorithm used in extracting the characteristic information not only was efficaciously and easily, but also had some advantages, such as high accuracy, stability and fast convergence rate. And it was useful in high precision measurement of FBG sensor.

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ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM

New Mathematics and Natural Computation ◽

10.1142/s179300570900143x ◽

2009 ◽

Vol 05 (02) ◽

pp. 487-496 ◽

Cited By ~ 12

Author(s):

WEI FANG ◽

JUN SUN ◽

WENBO XU

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm Optimization Algorithm ◽

Novel Mutation ◽

Particle Swarm ◽

Pso Algorithm ◽

Mutation Operator ◽

Fundamental Theory ◽

Swarm Optimization ◽

Mutation Operators ◽

Better Than

Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and implemented on the QPSO. Experiments are conducted on several well-known benchmark functions. Experimental results show that QPSO with some of the mutation operators is proven to be statistically significant better than the original QPSO.

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Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

Electronics ◽

10.3390/electronics8060603 ◽

2019 ◽

Vol 8 (6) ◽

pp. 603 ◽

Cited By ~ 1

Author(s):

Kuei-Hsiang Chao ◽

Cheng-Chieh Hsieh

Keyword(s):

Particle Swarm Optimization ◽

Artificial Bee Colony ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Pso Algorithm ◽

Global Maximum ◽

Photovoltaic Module ◽

Swarm Optimization ◽

Bee Colony ◽

Learning Factor

In this study, the output characteristics of partial modules in a photovoltaic module array when subject to shading were first explored. Then, an improved particle swarm optimization (PSO) algorithm was applied to track the global maximum power point (MPP), with a multi-peak characteristic curve. The improved particle swarm optimization algorithm proposed, combined with the artificial bee colony (ABC) algorithm, was used to adjust the weighting, cognition learning factor, and social learning factor, and change the number of iterations to enhance the tracking performance of the MPP tracker. Finally, MATLAB software was used to carry out a simulation and prove the improved that the PSO algorithm successfully tracked the MPP in the photovoltaic array output curve with multiple peaks. Its tracking performance is far superior to the existing PSO algorithm.

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An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis

Shock and Vibration ◽

10.1155/2020/1091548 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Xiaofeng Lv ◽

Deyun Zhou ◽

Ling Ma ◽

Yuyuan Zhang ◽

Yongchuan Tang

Keyword(s):

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Pso Algorithm ◽

Upper And Lower Bounds ◽

Swarm Optimization ◽

Multiple Fault ◽

Multiple Fault Diagnosis

The fault rate in equipment increases significantly along with the service life of the equipment, especially for multiple fault. Typically, the Bayesian theory is used to construct the model of faults, and intelligent algorithm is used to solve the model. Lagrangian relaxation algorithm can be adopted to solve multiple fault diagnosis models. But the mathematical derivation process may be complex, while the updating method for Lagrangian multiplier is limited and it may fall into a local optimal solution. The particle swarm optimization (PSO) algorithm is a global search algorithm. In this paper, an improved Lagrange-particle swarm optimization algorithm is proposed. The updating of the Lagrangian multipliers is with the PSO algorithm for global searching. The difference between the upper and lower bounds is proposed to construct the fitness function of PSO. The multiple fault diagnosis model can be solved by the improved Lagrange-particle swarm optimization algorithm. Experiment on a case study of sensor data-based multiple fault diagnosis verifies the effectiveness and robustness of the proposed method.

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A New Improved Adaptive Hybrid Particle Swarm Optimization Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1710 ◽

2013 ◽

Vol 427-429 ◽

pp. 1710-1713

Author(s):

Xiang Tian ◽

Yue Lin Gao

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization ◽

Searching Ability ◽

Learning Factors

This paper introduces the principles and characteristics of Particle Swarm Optimization algorithm, and aims at the shortcoming of PSO algorithm, which is easily plunging into the local minimum, then we proposes a new improved adaptive hybrid particle swarm optimization algorithm. It adopts dynamically changing inertia weight and variable learning factors, which is based on the mechanism of natural selection. The numerical results of classical functions illustrate that this hybrid algorithm improves global searching ability and the success rate.

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