Multi-objective Active Control Policy Design for Commensurate and Incommensurate Fractional Order Chaotic Financial Systems

TL;DR

This paper develops a multi-objective active control policy for fractional order chaotic financial systems, balancing stability and performance, and compares different evolutionary algorithms for optimal control design.

Contribution

It introduces a multi-objective control framework for fractional order financial systems and compares three evolutionary algorithms for Pareto optimal solutions.

Findings

Pareto front solutions reveal trade-offs between objectives.

Control robustness tested under fractional order variations.

Different MOO techniques yield comparable Pareto solutions.

Abstract

In this paper, an active control policy design for a fractional order (FO) financial system is attempted, considering multiple conflicting objectives. An active control template as a nonlinear state feedback mechanism is developed and the controller gains are chosen within a multi-objective optimization (MOO) framework to satisfy the conditions of asymptotic stability, derived analytically. The MOO gives a set of solutions on the Pareto optimal front for the multiple conflicting objectives that are considered. It is shown that there is a trade-off between the multiple design objectives and a better performance in one objective can only be obtained at the cost of performance deterioration in the other objectives. The multi-objective controller design has been compared using three different MOO techniques viz. Non Dominated Sorting Genetic Algorithm-II (NSGA-II), epsilon variable Multi-Objective Genetic Algorithm (ev-MOGA), and Multi Objective Evolutionary Algorithm with Decomposition (MOEA/D). The robustness of the same control policy designed with the nominal system settings have been investigated also for gradual decrease in the commensurate and incommensurate fractional orders of the financial system.