Computer Science ›› 2017, Vol. 44 ›› Issue (Z11): 123-128.doi: 10.11896/j.issn.1002-137X.2017.11A.025

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

LIU Jie, ZHAO Hai-fang and ZHOU De-lian   

  • Online:2018-12-01 Published:2018-12-01

Abstract: 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.

Key words: Path planning,Hybrid quantumbehaved particle swarm optimization,Mobile robots,Optimization algorithm

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