其他摘要：Due to its multiple applications, parameter identification for fractional-order chaotic systems has attracted the interests of several research communi ties. In the identification, the parameter estimation pro cess is transformed into a multidimensional optimization problem where fractional orders, as well as functio nal parameters of the chaotic system are considered the decision variables. Under this approach, the comple xity of fractional-order chaotic systems tends to produc e multimodal error surfaces for which their cost func tions are significantly difficult to minimize. Several al gorithms based on evolutionary computation principles have been successfully applied to identify the parameter s of fractional-order chaotic systems. However, most of them maintain an important limitation; they frequen tly obtain sub-optimal results as a consequence of an inappropriate balance between exploration and exploitation in their search strategies. This paper presents an algorithm for parameter identification of fractional-order chaotic systems. In order to deter mine the parameters, the proposed method uses the evolutionary method called Locust Search (LS), whic h is based on the behavior of swarms of locusts. Diff erent to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, eliminating critical flaws such as the premature convergence to sub-optimal solutions and the limited exploration-exploitation balance. Numer ical simulations have been conducted on the fractional- Order Van der Pol oscillator to show the effectiven ess of the proposed scheme.
