Numerical Solution of Dynamic Optimization Problems with Flexible Inequality Constraints by Adaptive Stochastic Algorithm

Abstract

An algorithm for solving nonlinear dynamic optimization problems subjected to flexible path constraints is proposed. By using the concept of fuzzy set to define the degree of acceptability for path constraints and degree of satisfaction for objective function, the original problem is transformed into a fuzzy decision making problem. To determine the optimum of the transformed system, fuzzy inference is incorporated into the computational platform, the integrated controlled random search for dynamic system (ICRS/DS). Because of the intrinsic structure, such an algorithm is very straightforward and efficient. Three examples are employed to demonstrate the simplicity and capability for the proposed algorithm.