Application of constraint infeasibility detection methods in engineering design problems

Abstract

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.

Advanced pest detection strategy using hybrid optimization tuned deep convolutional neural network

“…However, if the suppressed boxes comprise other objects, the suppression results in missing of some objects. Kasinathan et al (2020) modeled the machine learning and insect pest detection algorithm, which assists in reducing the computation time for better insect prediction but the recognition of multi-insects is not possible using this method. Pattnaik et al (2020) designed the transfer learning-based framework that reduced the cost of computation with the achievement of enhanced performance, but it undergoes the problem of over-fitting.…”

“…However, this process is highly time-consuming and so not preferable in most cases (Jiao et al , 2020). Attaining the pest classification with increased accuracy in the real-time field is a challenging task in the presence of dirt, leaves, branches, shadow, flower buds and so on in most agriculture crop fields (Kasinathan et al , 2020). …”

“…The LMPW constitutes various challenging issues contributing to the deterioration of the accuracy of the detection strategies. The detection method should exhibit higher accuracy of detection (De Souza-Daw and Ross, 2021;Khorshid et al, 2018) for the effective identification of suspicious behavior. The conventional approaches of pest detection work well in the labbased pest data set of small size but attain reduced accuracy and poor robustness when the data size increases.…”