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针对粒子群算法控制参数调整策略,提出一种双层粒子群优化算法(DBPSO);DBPSO有内外2层粒子,内层粒子是优化问题的解,对优化问题进行寻优;外层粒子由内层粒子的参数组成,通过协进化策略,实现参数自适应调整。利用5个常用标准测试函数对DBPSO的寻优性能进行测试,结果表明,其寻优性能优于基本PSO算法与一类改进的PSO算法(NMPSO)。最后,将DBPSO用于压力容器模型参数优化,取得了满意结果。
In order to solve the problem of optimization problem, aiming at the adjustment strategy of control parameters of particle swarm optimization, a double-layer particle swarm optimization algorithm (DBPSO) is proposed. DBPSO has two inner and outer particles, inner particle is the optimization problem, Layer particle parameter composition, through co-evolution strategy to achieve adaptive parameter adjustment. Five popular standard test functions were used to test the performance of the DBPSO. The results show that the performance of the proposed algorithm is superior to the basic PSO algorithm and the improved PSO algorithm (NMPSO). Finally, DBPSO is used to optimize the pressure vessel model parameters, and satisfactory results are obtained.