Constrained optimization using the chaotic sequential quadratic approximation type Lagrange quasi-Newton method

This study proposes a new global constrained optimization method for nonlinear constrained optimization problems that utilises the chaotic search trajectory generated by using the sequential quadratic approximation type Lagrange quasi-Newton search model, which is equivalent to the Lagrange quasi-Newton dynamics. Specifically, the chaotic search trajectory that can be utilized for the global search is generated through destabilization of the search trajectory of the sequential quadratic approximation type Lagrange quasi-Newton model based on the scenario similar to the scenario appearing in the chaotic optimization method for the unconstrained optimization problem. Then, a multi-point type search method in which the chaotic search trajectory is utilized is proposed. The effectiveness of the proposed method is confirmed through applications to several benchmark problems.