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中文摘要：
在分析量子粒子群算法的基础上,针对离散搜索空间的问题,提出了二进制编码的量子粒子群算法。在算法中,重新定义了粒子的位置距离矢量,调整了搜索空间的迭代方程,并引入了多点交叉和精英保留的策略,保证全局收敛的同时加快粒子的收敛速度。并使用De Jong’s测试函数对本算法和二进制粒子群算法进行了比较,最后使用二进制编码量子粒子群算法对机器人路径规划进行了仿真实验。
英文摘要：
Quantum-behaved particle swarm optimisation with binary encoding （BQPSO） was proposed to discrete binary search space. Position distance vector between two particles was redefined, the iterative equations to binary search space were adjusted, and multiple points crossover and elitist strategy were introduced to increase convergence speed with global convergence ability. After that, the BQPSO algorithm was tested on several De Jong＇s functions compared with binary PSO. Finally, simulation results of robot path planning with binary quantum-behaved particle swarm optimisation were discussed.
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