复合最优模型微粒群优化算法研究及应用

针对微粒群算法全局最优(Gbest)模型收敛速度快、局部搜索能力强、鲁棒性差,局部最优(Lbest)模型全局搜索能力强、鲁棒性好、收敛速度慢的特点,提出了一种结合全局最优和局部最优两基本模型特点的复合最优模型微粒群优化算法。用4个Benchmarks函数进行了测试,实验结果表明,与微粒群算法的两种基本模型相比,该复合模型算法能有效提高算法的收敛速度及全局搜索能力。最后将算法应用于一个非线性系统模型的辨识,辨识结果验证了该算法的有效性。

Research and application of compound optimum model particle swarm optimization

The global optimum model(Gbest) of particle swarm optimization(PSO) has characteristics of fast convergence and good local searching ability but poor robustness,while the local optimum model(Lbest) has characteristics of good global searching ability and robustness but slow convergence.Incorporating the characteristics of these two models,a compound optimum model PSO is proposed.Tests are done using four Benchmarks functions.The experimental results show that,compared with Gbest model and Lbest model,the strategy proposed can improve the convergence speed and global searching ability of the algorithm.Lastly the new algorithm is applied to identify a nonlinear system model,and the result proves the effectiveness of the algorithm.