In this study, a new optimization algorithm, called King, is introduced for solving variable order fractional optimal control problems (VO-FOCPs). The variable order fractional derivative is portrayed in the Caputo sense through the dynamics of the system as variable order fractional differential equation (VO-FDE). To this end, firstly, the VO-FOCP is converted into a system of VO-FDEs. Then, according to the fact that the VO-FDE is equivalent to a Volterra integral equation, the system of VO-FDEs is transformed into an equivalent system of variable order fractional integro-differential equations. In the next step, both the context of minimization of total error and a joint application of Banach's fixed-point theorem are used to solve a nonlinear optimization problem. Actually, using the existing mechanism, the synchronization problem is recast to an optimization problem. Due to the discretization and its board range of arbitrary nodes, the proposed method provides a very adjustable framework for direct trajectory optimization. Finally, the proposed algorithm is verified using several common optimization functions and a chaotic financial system. Also, through simulation results, the proposed method is compared with some popular methods. Simulation results demonstrate the appropriate performance of the introduced method.
King algorithm: A novel optimization approach based on variable-order fractional calculus with application in chaotic financial systems
Bekiros S.
2020-01-01
Abstract
In this study, a new optimization algorithm, called King, is introduced for solving variable order fractional optimal control problems (VO-FOCPs). The variable order fractional derivative is portrayed in the Caputo sense through the dynamics of the system as variable order fractional differential equation (VO-FDE). To this end, firstly, the VO-FOCP is converted into a system of VO-FDEs. Then, according to the fact that the VO-FDE is equivalent to a Volterra integral equation, the system of VO-FDEs is transformed into an equivalent system of variable order fractional integro-differential equations. In the next step, both the context of minimization of total error and a joint application of Banach's fixed-point theorem are used to solve a nonlinear optimization problem. Actually, using the existing mechanism, the synchronization problem is recast to an optimization problem. Due to the discretization and its board range of arbitrary nodes, the proposed method provides a very adjustable framework for direct trajectory optimization. Finally, the proposed algorithm is verified using several common optimization functions and a chaotic financial system. Also, through simulation results, the proposed method is compared with some popular methods. Simulation results demonstrate the appropriate performance of the introduced method.
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