Improved artificial gorilla troops optimizer with chaotic adaptive parameters - application to the parameter estimation problem of mixed additive and multiplicative random error models

Abstract

For mixed additive and multiplicative random error models (MAM models), due to the complex correlation between the parameters and the model power array, derivative operations will be inevitable in the actual calculation. When the observation equation is in nonlinear form, the operations will be more complicated. The swarm intelligence optimization algorithm (SIO) can effectively solve the derivative problem when estimating the nonlinear model parameters using conventional iterative algorithms. However, for different problems, the conventional SIO cannot effectively balance the ability of global and local behavior, resulting in the algorithm falling into prematureness and failing to output effective parameter information. To address the above problems, the improved artificial gorilla troops optimizer (CAGTO) algorithm with chaotic adaptive behavior is proposed. To address the problem that the population generated by the algorithm using pseudo-random numbers in the initialization population phase has poor traversability in the feasible domain, the chaotic sequence is applied to initialize the population instead of pseudo-random number generation to ensure that the population can traverse the feasible domain as much as possible and improve the global search capability of the algorithm. Adaptive parameters that vary linearly and nonlinearly with the algorithm process are constructed to balance the global search and local search ability, while accelerating the convergence speed. Two CAGTO algorithms with different parameter settings are constructed for different problems, and the experimental results show that both CAGTO algorithms can effectively solve the parameter estimation problem of MAM models with different nonlinear forms of observation equations compared with several other comparative algorithms.

Damped least squares method for nonlinear mixed additive and multiplicative errors model

“…Wang et al [14] derived the weighted least squares regularization iterative solution and mean square error matrix of the ill-conditioned mixed additive and multiplicative errors model. A modified artificial gorilla force optimizer algorithm with chaotic adaptive behavior for mixed additive and multiplicative errors model was studied in the literature [15].…”

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

“…Finally, quasi-reflective learning generates the quasi-reflective position of silver-backed gorillas 29 . Wang et al proposed an enhanced gorilla, using circle chaotic mapping to increase gorilla population diversity and applying it to clustering protocols in unmanned aerial vehicle-assisted intelligent vehicle networks 30 . Mostafa et al 31 introduced elite reverse learning to enhance population diversity, using the fusion of Cauchy inverse cumulative distribution operator and tangent flight to improve population development ability, thereby increasing convergence speed.…”

Helmert variance component estimation for mixed additive and multiplicative random error model

“…In [12] studied the parameter estimation of the observation-dependent multiplicative error model. In [13,14] applied the intelligent search algorithm to the model and improved the parameter estimation theory of MAMREM. The improved derivative-free artificial bee colony algorithm and bootstrap method studied by [15] can obtain better parameter estimation and more reasonable accuracy information than the weighted least squares method.…”