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针对混沌系统未知参数的辨识问题,结合人工蜂群搜索算子和混沌优化策略,提出一种自适应混合引力搜索算法,并应用于混沌系统未知参数的优化辨识.利用混沌序列初始化种群以增强搜索初期的遍历性,基于人工蜂群搜索算子进行变异操作以提高算法的局部寻优能力,依据粒子的性能对进化过程中的万有引力系数进行自适应调整,有效避免了早熟收敛,提高了算法的整体寻优性能.以测试函数和典型混沌系统为例进行仿真实验,结果证明该算法具有良好的全局探测和局部开发能力,与遗传算法、粒子群算法、量子粒子群算法和引力搜索算法比较,其对混沌系统参数的估计具有相对较高的辨识精度和收敛速度,算法的有效性得到了验证.
Aiming at the problem of unknown parameter identification of chaotic system, an adaptive hybrid gravitational search algorithm based on artificial bee colony search operator and chaos optimization strategy is proposed and applied to the optimal identification of unknown parameters of chaotic system. The chaos sequence is used to initialize the population to enhance the search The initial ergodicity is based on the artificial bee colony search operator mutation operation to improve the local optimization ability of the algorithm, adaptive adjustment of the gravitational coefficient in the evolutionary process according to the performance of the particle, effectively avoiding premature convergence, and improve the algorithm’s The whole optimization performance is tested by using the test function and the typical chaotic system as an example. The simulation results show that this algorithm has a good ability of global exploration and local development. Compared with genetic algorithm, particle swarm optimization, quantum particle swarm optimization and gravitation search algorithm, Its estimation of chaotic system parameters has a relatively high recognition accuracy and convergence speed, the validity of the algorithm has been verified.