Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning

Authors

Xiangyin Zhang^{*, 1, 3, 4,} , Shuang Xia^{1, 2, 4}, Xiuzhi Li^{1, 2, 4}

¹Faculty of Information Technology, Beijing University of Technology, Beijing 100124, PR China

²Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, PR China

³Beijing Laboratory for Urban Mass Transit, Beijing 100124, PR China

⁴Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, PR China

^*Corresponding author. Email: zhangxgyn@foxmail.com

DOI

10.2991/ijcis.d.200825.001How to use a DOI?

Keywords

Fruit fly optimization algorithm; Continuous function optimization; Delta potential well; Quantum behavior; Path planning

Abstract

As a newly developed simple and effective optimization technology, the fruit fly optimization algorithm (FOA) has been successfully applied in many fields. To accelerate the algorithm convergence and avoid the local optimum, the enhanced FOA based on quantum theory called QFOA is proposed in this paper. When establishing the quantum Delta potential well around the location of fruit fly swarm, QFOA introduces the quantum behavior-based searching mechanism into the original osphresis-based search procedure of FOA. In the process that fruit flies find and move toward the food source, fruit flies follow the wave function property of the Delta potential well rather than the Newtonian mechanics. Taking advantage of the probability and uncertainty of quantum theory, the proposed QFOA can effectively overcome the weakness in premature convergence and easy trapping into local optimum. Since there are two popular models of the basic FOA, this paper also develops two corresponding QFOAs. Experimental results on various benchmark functions show that both the two QFOA models has overall better performance compared with the basic FOA as well as other FOA variants and other well-known optimization algorithms. In addition, the proposed QFOAs are also applied to unmanned aerial vehicle (UAV) path planning problem in the three-dimensional environment, and comparative results about the obtained optimal flight path and population convergence process show the effectiveness of QFOAs.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

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

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TY  - JOUR
AU  - Xiangyin Zhang
AU  - Shuang Xia
AU  - Xiuzhi Li
PY  - 2020
DA  - 2020/09/07
TI  - Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning
JO  - International Journal of Computational Intelligence Systems
SP  - 1315
EP  - 1331
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200825.001
DO  - 10.2991/ijcis.d.200825.001
ID  - Zhang2020
ER  -