scholarly journals Multiconstrained Ascent Trajectory Optimization Using an Improved Particle Swarm Optimization Method

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mu Lin ◽  
Zhao-Huanyu Zhang ◽  
Hongyu Zhou ◽  
Yongtao Shui

This paper researches the ascent trajectory optimization problem in view of multiple constraints that effect on the launch vehicle. First, a series of common constraints that effect on the ascent trajectory are formulated for the trajectory optimization problem. Then, in order to reduce the computational burden on the optimal solution, the restrictions on the angular momentum and the eccentricity of the target orbit are converted into constraints on the terminal altitude, velocity, and flight path angle. In this way, the requirement on accurate orbit insertion can be easily realized by solving a three-parameter optimization problem. Next, an improved particle swarm optimization algorithm is developed based on the Gaussian perturbation method to generate the optimal trajectory. Finally, the algorithm is verified by numerical simulation.


2014 ◽  
Vol 615 ◽  
pp. 270-275
Author(s):  
Wen Jing Zhang ◽  
Fen Fen Xiong

Glide trajectory optimization of vehicle can greatly improve the performance of missile. As is well-known, methods of trajectory optimization can be divided into direct and indirect methods. Generally, the direct method is convenient and can obtain the optimal solution with higher probability. Based on the direct method, a missile trajectory is optimized by discretizing the control quantity (angle of attack) and transforming the original optimal control problem to a nonlinear programing problem (NLP) in the present paper. The particle swarm optimization algorithm that is easy to implement and has higher convergence rate is utilized to solve the transformed NLP to generate the optimal angle of attack rule. Simulation results show that with the optimal rule, gliding distance of missile is clearly improved compared to the initial one.





Author(s):  
Kummari Rajesh ◽  
N. Visali

In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multi-objective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.



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