Heurestic Optimization-Based Trajectory Optimization

Runqi Chai*, Kaiyuan Chen, Lingguo Cui, Senchun Chai, Gokhan Inalhan, Antonios Tsourdos

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Özet

Conventional optimization methods have certain problems in finding the optimal solution. The feasible solution space of a trajectory optimization model may be constrained to a relatively limited corridor due to numerous mission-related constraints, easily leading to local minimum or infeasible solution identification. This section focuses on an attempt to use a biased particle swarm optimization method to solve the constrained trajectory design problem. By adding a normalized objective that reflects the entire quantity of constraint violations, the suggested method reformulates the original issue into an unconstrained multi-criteria version. The algorithm also includes a local exploration operation, a novel-bias selection method, and an evolution restart strategy to speed up progress during the evolutionary process. The success of the suggested optimization technique is confirmed by numerical simulation experiments that were generated from a confined atmospheric entry trajectory optimization example. Executing a number of comparative case studies also demonstrates the main benefits of the suggested strategy.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıSpringer Aerospace Technology
YayınlayanSpringer Science and Business Media Deutschland GmbH
Sayfalar43-75
Sayfa sayısı33
DOI'lar
Yayın durumuYayınlandı - 2023
Harici olarak yayınlandıEvet

Yayın serisi

AdıSpringer Aerospace Technology
HacimPart F1477
ISSN (Basılı)1869-1730
ISSN (Elektronik)1869-1749

Bibliyografik not

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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