Parameter estimation of chaotic systems by an oppositional seeker optimization algorithm

The parameter estimation can be formulated as a multi-dimensional optimization problem. By combining the seeker optimization algorithm with the opposition-based learning method, an oppositional seeker optimization algorithm is proposed in this work, and is applied to the parameter estimation of chaotic systems. The seeker optimization algorithm provides a new alternative for population-based heuristic search. By considering an estimate and its opposite of current solutions at the same time, the opposition-based learning method is employed for population initialization and also for generation jumping in seeker optimization algorithm. Numerical simulations on two typical chaotic systems are conducted to show the effectiveness and robustness of the proposed scheme.