An imperialist competitive algorithm for solving dynamic nonlinear constrained optimization problems

摘要

Dynamic nonlinear constrained optimization problems (DNCOP) have been arisen in a diverse range of sciences, such as agriculture, economics, airspace engineering, chemistry, mechanics, management etc.. In order to solve DNCOP, we are interested in the designed algorithm not only to find the optimal solutions or the quasi-optimum solutions, but recover and track the trajectory of the optimal solutions changing with the time. In this paper, the considering DNCOP is transformed into a dynamic unconstrained optimization problem by adding the slack variables to the inequations constraint of the original problem firstly. Secondly, an improved imperialist competitive optimization algorithm for solving the DNCOP is proposed. At last, the computation simulations show that the proposed algorithm is more effective and can find the better optimal solutions or the quasi-optimum solutions in environment-varying than the compared algorithms for dynamic nonlinear constrained optimization problem.