Engineering Design Optimization Using Interior-Point Algorithms

Abstract

The interior-point methods have emerged as highly efficient procedures for solving linear programming problems in recent years. The interior point methods have superior theoretical properties as well as observed computational advantages over simplex methods at solving large linear programming problems and are found to be immune to degeneracy. It is a common observation in literature that most nonlinear programming methods that work well for small and medium sized problems are not able to solve large-scale problems efficiently. It is to be expected that interior-point methods, when used in an adaptive sequential linear programming strategy, might prove to be a powerful engineering optimization tool. The development of an adaptive sequential linear programming algorithm, based on an infeasible primal-dual path-following interior-point algorithm and fuzzy heuristics, is considered in this work for the solution of large-scale engineering design optimization problems. Several numerical examples are considered to demonstrate the effectiveness and efficiency of the present method. The examples include a part stamping problem, a turbine rotor design problem, and an optimal control problem related to tidal power generation