Abstract
Genetic algorithms (GA) and ant colony algorithm (ACO) are computational models that simulate the process of biological evolution and are widely applied to problems related to path planning. This paper proposes a path planning algorithm which combines the genetic algorithm and the ant colony algorithm. The hybrid algorithm overcomes the problem of genetic algorithm's poor ability in local search, and overcomes the slow convergence speed, local optimal solution of ant colony algorithm's in the process of convergence. The effectiveness and rapidity of the proposed hybrid algorithm is verified by simulation and experiment. The overall performance is better than the genetic algorithm and the ant colony algorithm. Finally, we carried out the experiments to verify the effectiveness of the proposed hybrid algorithm which could avoid collision and reach the target safely, and the proposed hybrid algorithm was compared with ACO and GA respectively. The experimental results showed that the proposed hybrid algorithm overcame the defects of ACO and GA, verified the effectiveness and rapidity of the proposed hybrid alaorithm,
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