A competitive game optimization algorithm for Unmanned Aerial Vehicle path planning

TL;DR

The paper tackles UAV path planning and related engineering optimization by introducing the Competitive Game Optimizer (CGO), a population-based metaheuristic inspired by competitive game dynamics. CGO integrates initialization, Levy-flight-driven search, battle via adaptive encounter probability, and an explicit safe-zone advancement to balance exploration and exploitation, with mathematical updates for candidate solutions. Empirical results on 41 CEC2017/CEC2022 benchmarks and 8 real-world problems show CGO achieving competitive or superior performance (supported by Wilcoxon tests) and demonstrating strong convergence and robustness, including in UAV path planning scenarios where it outperforms several baselines. The findings suggest CGO's practical utility for complex engineering design tasks and its potential applicability to broader domains such as image segmentation, highlighting its simple structure and strong optimization capability.

Abstract

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.

Figures (39)

• Figure 1: Overworld of the game
• Figure 2: Safety zone refresh chart
• Figure 3: Levy flight path in 2-dimensional space
• Figure 4: Convergence curves of 8 algorithms in $\rm CEC2017_1$
• Figure 5: Convergence curves of 8 algorithms in $\rm CEC2017_3$