Design of chaotic Young's double slit experiment optimization heuristics for identification of nonlinear muscle model with key term separation

Abstract

In this work, a novel variant of Young's double slit experiment (YDSE) optimizer is introduced with improved performance by integrating ten different chaotic maps. The integration is performed in three different ways and thirty chaotic variants of YDSE optimizer are proposed. The analysis is performed on mathematical and CEC benchmark functions having unimodal and multimodal features. It is further applied to electrically stimulated muscle model which is generalization of input nonlinear Hammerstein controlled autoregressive model with key term separation used for patients with spinal cord injury. The results indicates that chaotic maps enhance the performance of YDSE optimizer. More specifically integration of Gauss map in both exploration and exploitation mechanisms (M3CYDSE3) is most effective than other variants. Detailed convergence analysis, statistical executions, complexity analysis and Freidman test show that M3CYDSE3 achieves best performance against artificial electric field algorithm (AEFA), arithmetic optimization algorithm (AOA), propagation search algorithm (PSA), particle swarm optimization (PSO), sine cosine algorithm (SCA), and YDSE optimizer.