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针对群体智能算法理论基础、缺乏普遍意义的理论分析等问题,提出了一种高性能的广义切线混沌优化算法(GTC).该算法是基于空间域搜索的寻优算法,利用广义切线法、混沌算子和空间域搜索的特性来提高算法的全局寻优能力和收敛速度.为验证该算法的性能,与非线性递减权重粒子群算法(NDWPSO)、人工鱼群算法(AFSA)和实数编码的遗传算法(GA)进行对比,分别对3个测试函数、PID参数整定和一个高度非线性系统参数估计三个实例进行分析比较.研究结果表明,所提出的广义切线混沌算法具有大范围搜寻空间内的全局优化能力和快速收敛性.
Aiming at the theoretical basis of group intelligence algorithm and the lack of generalized theoretical analysis, this paper proposes a high-performance generalized tangent chaos optimization algorithm (GTC). This algorithm is based on spatial domain search optimization algorithm. By using generalized tangent method, chaos Operator and space domain search to improve the global optimization ability and convergence speed of the algorithm.In order to verify the performance of the algorithm, the algorithm is compared with the non-linear descending weighted particle swarm optimization (NDWPSO), artificial fish swarm algorithm (AFSA) Genetic Algorithm (GA) is used to compare and analyze the three test functions, PID parameter tuning and a highly nonlinear system parameter estimation.The results show that the proposed generalized tangent chaos algorithm has a large range of search space Global optimization ability and fast convergence.