An Efficient Computational Cost Reduction Strategy for the Population-Based Intelligent Optimization of Nonlinear Dynamical Systems

Population-based intelligent optimization algorithms are popular due to their global optimality. However, it will be time-consuming if they are directly applied to nonlinear dynamical systems. In this article, an efficient computational cost reduction strategy is presented for the population-based intelligent optimization algorithms when they are employed to search for the global optimum of nonlinear systems in dynamic equilibrium. Specifically, a novel constrained optimization problem is formulated according to the demand of dynamic equilibrium in industry, and the reason why population-based methods take much more computing time is analyzed from the perspective of solving nonlinear equations. Since the computational complexity of solving nonlinear equations is sensitive to their initial values, a sensitivity transfer condition is provided to explain the significance of candidate testing order. Finally, an optimal evaluation sequence, which minimizes the Euclidean distance of adjacent candidates, is designed for the population-based intelligent optimization algorithms. Simulations show the effectiveness.