一种改进量子行为粒子群优化算法的移动机器人路径规划

为实现移动机器人最优路径规划,提出了一种改进量子行为粒子群的优化算法(LTQPSO)。针对粒子群算法存在过早收敛的问题,利用个体粒子进化速度与群体离散度来动态调整惯性权重,使惯性权重具有自适应性与控制性,从而避免过早收敛；同时将自然选择方法引入传统位置更新公式中,以保持种群的多样性,加强LTQPSO算法的全局搜索能力,加快算法的收敛速度；将改进后的LTQPSO算法应用于移动机器人路径规划中；最后通过理论仿真与移动机器人平台实验验证了该方法的有效性与可行性。

Improved Quantum Behaved Particle Swarm Optimization Algorithm for Mobile Robot Path Planning

In order to realize the optimal path planning of mobile robot,an improved quantum behaved particle swarm optimization (LTQPSO) algorithm was proposed.Aiming at that the particle swarm algorithm has the problem of premature convergence,the individual particle evolution speed and the group dispersion are used to dynamically adjust the inertia weight,which makes the inertia weight adaptive and controllabe,and avoids premature convergence.Meanwhile,the natural selection method was introduced into the traditional location update formula in order to maintain the population diversity,strengthen the search ability of the global QPSO algorithm,and improve the convergence speed of the algorithm.The improved QPSO algorithm was applied to the path planning of mobile robot.Finally,the effectiveness and feasibility of the proposed method was verified by theoretical simulation and experimental results of a mobile robot platform.