A competitive game optimization algorithm for Unmanned Aerial Vehicle path planning
arxiv(2024)
摘要
To solve the Unmanned Aerial Vehicle (UAV) path planning problem, a
meta-heuristic optimization algorithm called competitive game optimizer (CGO)
is proposed. In the CGO model, three phases of exploration and exploitation,
and candidate replacement, are established, corresponding to the player's
search for supplies and combat, and the movement toward a safe zone. In the
algorithm exploration phase, Levy flight is introduced to improve the global
convergence of the algorithm. The encounter probability which adaptively
changes with the number of iterations is also introduced in the CGO. The
balance between exploration and exploitation of solution space of optimization
problem is realized, and each step is described and modeled mathematically. The
performance of the CGO was evaluated on a set of 41 test functions taken from
CEC2017 and CEC2022. It was then compared with eight widely recognized
meta-heuristic optimization algorithms. The simulation results demonstrate that
the proposed algorithm successfully achieves a balanced trade-off between
exploration and exploitation, showcasing remarkable advantages when compared to
seven classical algorithms. In addition, in order to further verify the
effectiveness of the CGO, the CGO is applied to 8 practical engineering design
problems and UAV path planning, and the results show that the CGO has strong
performance in dealing with these practical optimization problems, and has a
good application prospect.
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