Solving A Multi-objective Mission Planning Problem for UAV Swarms with An Improved NSGA-III Algorithm

DOI

10.2991/ijcis.11.1.81How to use a DOI?

Keywords

mission planning; UAV swarms; motif; adaptive genetic operators; NSGA-III algorithm; optimization

Abstract

Restricted communication in unmanned aerial vehicle (UAV) swarms means that configuration needs to vary dynamically with changing tasks. We propose a mission planning model that uses a motif, a grouping of related functions, as the basic task unit. The planning model automatically generates a mission planning scheme from a task priority execution order given as an input. The selection of the best scheme from among possible solutions is a multi-objective optimization problem with calculation complexity rapidly increasing with the number of tasks. To address this difficulty, we enhance the NSGA-III algorithm by adding adaptive genetic operators when generating the offspring population. We apply the improved NSGA-III algorithm to optimize mission planning schemes with changing task priority execution orders. We validated the feasibility and effectiveness of the improved algorithm via a case study.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Jiajie Liu
AU  - Weiping Wang
AU  - Xiaobo Li
AU  - Tao Wang*
AU  - Senyang Bai
AU  - Yanfeng WANG
PY  - 2018
DA  - 2018/05/28
TI  - Solving A Multi-objective Mission Planning Problem for UAV Swarms with An Improved NSGA-III Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 1067
EP  - 1081
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.81
DO  - 10.2991/ijcis.11.1.81
ID  - Liu2018
ER  -