Can flexible constraints be optimized in nonlinear dynamic systems? This article introduces an innovative algorithm designed to optimize nonlinear dynamic systems that are subject to flexible path constraints. It addresses challenges where traditional optimization methods may struggle due to the imprecise nature of these constraints. The approach involves fuzzifying each flexible constraint using the concept of degree-of-acceptability and derives the fuzzy degree-of-satisfaction for the objective. The numerical method of Integrated Controlled Random Search for Dynamic System (ICRS/DS) is applied for solving the resulting fuzzy decision problem. A numerical example demonstrates the proposed algorithm's applicability and potential in addressing complex optimization challenges. This could contribute to advances in various fields that require precise management of nonlinear dynamic systems.
Published in the International Journal on Artificial Intelligence Tools, this paper is relevant to the journal's focus on intelligent systems and computational methods. It aligns with the journal's scope by presenting a novel algorithm for solving complex optimization problems and demonstrates the practical utility of AI tools in dynamic systems.