CAJ | 학술논문

量子遗传算法具有适应性强、收敛速度快、适合于全局搜索的特点，粒子群优化算法的优点是具有记忆能力，在智能搜索的实现上可以结合个体和全局的最佳位置实现位置定位，但粒子群优化算法在搜索速度和择优能力方面还有待提升。因此提出了一种改进的路径规划算法，即利用量子遗传算法结合粒子群优化算法的记忆功能和最佳定位能力，实现对移动机器人路径规划算法的改进。通过仿真实验已经证明，改进后的移动机器人路径规划算法在稳定性和路径优化选择上都优于单纯的粒子群优化算法和量子遗传算法，并且改进后的算法更适合于复杂路径中实现优化。
양자유전산법구유괄응성강、수렴속도쾌、괄합우전국수색적특점，입자군우화산법적우점시구유기억능력，재지능수색적실현상가이결합개체화전국적최가위치실현위치정위，단입자군우화산법재수색속도화택우능력방면환유대제승。인차제출료일충개진적로경규화산법，즉이용양자유전산법결합입자군우화산법적기억공능화최가정위능력，실현대이동궤기인로경규화산법적개진。통과방진실험이경증명，개진후적이동궤기인로경규화산법재은정성화로경우화선택상도우우단순적입자군우화산법화양자유전산법，병차개진후적산법경괄합우복잡로경중실현우화。
The quantum genetic algorithm has strong adaptability,fast convergence speed,and is suitable for global search. The advantages of particle swarm optimization algorithm are the memory capacity,and the fixed position with individual and global best location which can be implemented in the intelligent search. However,the search speed and preferred ability of the particle swarm optimization algorithm still need to be improved. Therefore,we put forward a kind of path planning algorithms,namely the quantum genetic algorithm combined with memory function and the best orientation ability of particle swarm optimization algorithm,are used to improve the path planning algorithm of mobile robot. The simulation experiment has proved that the improved path planning algorithm of mobile robot is better than simple particle swarm optimization algorithm and quantum genetic algorithm in the stability and choice of path optimization,and the improved algorithm is more suitable for implementation in complex path optimization.