TY - JOUR
T1 - UAV Path Planning in Presence of Occlusions as Noisy Combinatorial Multi-objective Optimisation
AU - Aishwaryaprajna, Aishwaryaprajna
AU - Kirubarajan, Thia
AU - Tharmarasa, Ratnasingham
AU - Rowe, Jon
PY - 2023/8/9
Y1 - 2023/8/9
N2 - A realistic noisy combinatorial problem on surveillance by Unmanned Aerial Vehicle (UAV) in presence of weather factors is defined. The presence of cloud coverage is considered as a posterior Gaussian noise in the visibility region of the UAV. Recent studies indicate that recombination-based search mechanisms are helpful in solving noisy combinatorial problems. The search strategy of Univariate Marginal Distribution Algorithm that includes only selection and recombination, which has a close association with genepool crossover, proves to be beneficial in solving constrained and multi-objective combinatorial problems in presence of noise. This paper proposes a solution methodology based on Multi-objective UMDA (moUMDA) with diversification mechanisms for the multi-objective problem of UAV surveillance. To obtain a well- spread set of Pareto optimal solutions, relevant diversification mechanisms are important. Numerical simulations show that moUMDA with and without K-Means clustering provides better quality solutions and a more diverse Pareto optimal set than NSGA-II in solving this noisy problem.
AB - A realistic noisy combinatorial problem on surveillance by Unmanned Aerial Vehicle (UAV) in presence of weather factors is defined. The presence of cloud coverage is considered as a posterior Gaussian noise in the visibility region of the UAV. Recent studies indicate that recombination-based search mechanisms are helpful in solving noisy combinatorial problems. The search strategy of Univariate Marginal Distribution Algorithm that includes only selection and recombination, which has a close association with genepool crossover, proves to be beneficial in solving constrained and multi-objective combinatorial problems in presence of noise. This paper proposes a solution methodology based on Multi-objective UMDA (moUMDA) with diversification mechanisms for the multi-objective problem of UAV surveillance. To obtain a well- spread set of Pareto optimal solutions, relevant diversification mechanisms are important. Numerical simulations show that moUMDA with and without K-Means clustering provides better quality solutions and a more diverse Pareto optimal set than NSGA-II in solving this noisy problem.
KW - noisy combinatorial optimisation
KW - posterior additive noise
KW - UAV path planning
KW - multi-objective optimisation
KW - clustering
U2 - 10.1504/IJBIC.2023.10057556
DO - 10.1504/IJBIC.2023.10057556
M3 - Article
SN - 1758-0366
VL - 21
JO - International Journal of Bio-Inspired Computation
JF - International Journal of Bio-Inspired Computation
IS - 4
ER -