The theoretical foundation of swarm intelligence algorithms is relatively weak, and a general theoretical analysis has not yet been conducted. Thus, this study proposes a high-performance generalized tangent-chaos (GTC) optimization algorithm based on the spatial domain. In the algorithm, characteristics of the generalized tangent method, chaos operator, and searching in the spatial domain are applied to enhance the global search ability and convergence speed. To verify the performance of the GTC optimization algorithm, it is compared with the nonlinearly decreasing weight PSO, artificial fish school algorithm, and real-coded genetic algorithm. Three well-known benchmark functions, the tuning of PID controller parameters, and the parameter estimation of a highly nonlinear system are utilized to test the ability of the GTC optimization algorithm. Results show that the proposed GTC optimization algorithm has excellent global optimization performance and convergence speed.
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