Quasi-oppositional-based learning Schrödinger algorithm for weight optimization of the aircraft torque link component

Abstract

This work presents an improved Schrödinger-based optimization approach incorporating quasi-oppositional learning to enhance solution quality in structural weight reduction problems. The method was applied to the design of an aircraft torque link, where mass minimization was performed under a maximum allowable stress of 250 MPa using finite element analysis. The optimized configuration reduced the mass from 4.053 kg to 3.115 kg, corresponding to a 23.14 % decrease, while the maximum stress reached 239 MPa and remained within safe limits. Under identical computational effort (500 function evaluations), the proposed approach achieved lower mass values than other algorithms. The results indicate that the developed strategy is well-suited for lightweight aerospace components and similar high-performance engineering applications.