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提出一种改进的粒子群算法,在粒子进化过程中将种群中粒子两个为一对,分成若干对.每次进化之后对每对粒子中的两个粒子进行比较,代价函数小的粒子作为较优粒子正常进化,代价函数大的粒子作为次优粒子,进化时速度变量按一定概率进行变异.通过测试函数对改进粒子群算法性能的验证表明改进粒子群算法具有较好的搜索精度、稳定性及搜索速度.将改进粒子群算法用于无人水下航行器(UUV)三维航迹规划中,仿真结果表明:利用粒子群算法寻找航迹规划代价函数最小点,得到下一规划点,从而实现航迹规划,取得了较好的效果.
An improved particle swarm optimization algorithm is proposed, in which two particles in a population are paired into several pairs, each particle pair is compared after each evolution, and particles with small cost function are used as The optimal particles are normal evolution and the particles with large cost function are used as suboptimal particles, and the speed variable is evolved by a certain probability when it is evolved.Through the test function to verify the performance of the improved particle swarm optimization algorithm, the improved particle swarm optimization algorithm has better searching accuracy and stability And search speed.The improved Particle Swarm Optimization (PSO) is applied to the UAV 3D trajectory planning.The simulation results show that the particle swarm optimization algorithm is used to find the minimum point of the trajectory planning cost function, and the next planning point is obtained, In order to achieve the track planning, and achieved good results.