核心概念
A meta-heuristic optimization algorithm called competitive game optimizer (CGO) is proposed to solve the Unmanned Aerial Vehicle (UAV) path planning problem.
摘要
The paper proposes a new meta-heuristic optimization algorithm called the competitive game optimizer (CGO) to solve the Unmanned Aerial Vehicle (UAV) path planning problem.
The CGO algorithm is inspired by the game mechanics and player behaviors in a competitive military game. It simulates the actions of players searching for supplies, engaging in battles, and moving towards the safe zone. The algorithm incorporates three key phases: exploration and exploitation, candidate replacement, and movement towards the safe zone.
In the exploration and exploitation phase, the algorithm uses Levy flight to model the players' search for supplies, with the step size adaptively changing based on the iteration number. The battle phase simulates player encounters and the resulting combat strategies. The movement towards the safe zone phase encourages players with poor objective function values to move towards the best individual.
The performance of the CGO algorithm is evaluated on a comprehensive set of 41 benchmark functions from the CEC2017 and CEC2022 test suites. Comparisons are made with 7 other widely recognized meta-heuristic optimization algorithms. The results demonstrate that the CGO algorithm achieves a good balance between exploration and exploitation, and outperforms the other algorithms on many of the test functions.
The CGO algorithm is also applied to 8 practical engineering design optimization problems and the UAV path planning problem. The simulation results show that the CGO algorithm has strong performance in dealing with these real-world optimization problems and has good application prospects.
統計資料
The best, standard deviation, and mean values obtained by the 8 algorithms on the CEC2017 and CEC2022 test suites are provided in the tables.