Two-point mid-range approximation enhanced recursive quadratic programming method

This research represents an attempt to combine good convergence properties of recursive quadratic programming methods with the benefits of mid-range approximations, initially developed in the field of structural optimization. In this paper, an optimization method based on Arora and coworkers' PLBA (Pshenichny–Lim–Belegundu–Arora) algorithm is proposed in which, during the line search phase, cost and constraint functions are substituted by their two-point approximations using the Generalized Convex Approximation formulae of Chickermane and Gea. The results showed that the proposed optimization method preserves the reliability and accuracy of the recursive quadratic programming method while it might simultaneously reduce the computational effort for some problems. Therefore, the proposed optimization method may be taken as potentially suitable for general design optimization purposes.