Oppositional backtracking search optimization algorithm for parameter identification of hyperchaotic systems

Parameter identification is an important issue in nonlinear science and has received increasing interest in the recent years. In this paper, an oppositional backtracking search optimization algorithm is proposed to solve the parameter identification of hyperchaotic system. The backtracking search optimization algorithm provides a new alternative for population-based heuristic search. To increase the diversity of initial population and to accelerate the convergence speed, the opposition-based learning method is employed in the backtracking search optimization algorithm for population initialization as well as for generation jumping. Numerical simulations on several typical hyperchaotic systems are conducted to demonstrate the effectiveness and robustness of the proposed scheme.

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