Application of constraint infeasibility detection methods in engineering design problems

Purpose: The purpose of this paper is to present numerical experimentation of three constraint detection methods to explore their main features and drawbacks in infeasibility detection during the design process. Design/methodology/approach: Three detection methods (deletion filter, additive method and elasticity method) are used to find the minimum intractable subsystem of constraints in conflict. These methods are tested with four enhanced NLP solvers (sequential quadratic program, multi-start sequential quadratic programing, global optimization solver and genetic algorithm method). Findings: The additive filtering method with both the multistart sequential quadratic programming and the genetic algorithm solvers is the most efficient method in terms of computation time and accuracy of detecting infeasibility. Meanwhile, the elasticity method has the worst performance. Research limitations/implications: The research has been carried out for only inequality constraints and continuous design variables. This research work could be extended to develop computer-aided graphical user interface with the capability of including equality constraints and discrete variables. Practical implications: These proposed methods have great potential for finding and guiding the designer to detect the infeasibility for ill-posed complex design problems. Originality/value: The application of the proposed infeasibility detection methods with their four enhanced solvers on several mechanical design problems reduces the number of constraints to be checked from full set to a much smaller subset.