A hybrid adaptive quadratic interpolation optimization algorithm for benchmark engineering design problems

Metaheuristic algorithms have now become critical in solving nonlinear and constrained engineering design problems that are hard to solve by traditional optimization methods. Quadratic interpolation optimization (QIO) is a new technique that is founded on interpolation principles, yet its effectiveness is frequently restricted by an early convergence and a lack of local refinement when used on real-life problems. A better version of this is proposed in this research and is known as Adaptive quadratic interpolation optimization (AQIO). AQIO complements QIO in three ways: adaptive parameter adjustment to achieve a smooth trade-off between exploration and exploitation, elite dynamic opposition (EDO) to preserve the diversity of the population, and cauchy perturbation to reinforce local exploitation. Six popular engineering design problems, such as spring design, pressure vessel design, welded beam design, speed reducer design, gear train design, and three-bar truss optimization, are used to test the performance of AQIO. Comparative findings indicate that AQIO is always better than the original QIO and some of the known optimization algorithms in terms of quality of solution, speed of convergence, and strength. These results show that AQIO is an effective and trustworthy optimization method to solve constrained engineering design problems.