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In order to realize the optim a l path planning o f m obile ro b o t, an im proved quantum behaved partic le swarm op tim iza tion (L T Q P S O ) a lg o rith m was proposed. A im in g at tha t the partic le sw arm a lg o rith m has the prob lem o f pre­ m ature convergence, the ind iv idua l partic le evo lu tion speed and the group dispersion are used to dynam ica lly adjust the ine rtia w e ig h t, w h ich makes the ine rtia w e igh t adaptive and contro llabe ,and avoids prem ature convergence. M eanw h ile, the natura l selection m ethod was in troduced in to the trad itio n a l location update fo rm u la in order to m ainta in the popula­ tio n d iv e rs ity, strengthen the search a b ility o f the global QPSO a lg o rith m , and im prove the convergence speed o f the al­ g o rith m . T he im proved QPSO a lg o rith m was applied to the path planning o f m obile robo t. F in a lly ,th e effectiveness and fe a s ib ility o f the proposed m ethod was ve rified by theoretica l s im u la tion and experim ental resu lts o f a m obile robo t p la t­ fo rm .

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