Decision-Making in Complicated Geometrical Problems

Abstract

Due to increasing the number of decision-making criteria in today's ever complicated geometrical optimization problems, the traditional multiobjective optimization approaches, whether a priori, a posteriori or interactive's, found to be insufficient and ineffective. In this paper the drawbacks of the current algorithms are reviewed and the urgent need for inserting a learning component in the optimization loop is discussed. In the following the methodology of reactive optimization for evolutionary interactive multiobjective optimization for solving complicated geometrical decisionmaking problems is adopted. The proposed brain-computer optimization follows to the paradigm of learning while optimizing, through the use of online machine learning techniques as an integral part of a self-tuning optimization scheme. At the end the effectiveness of the approach to geometrical problems is emphasized by providing the study case of optimal design problem of curves and surfaces.

Identification of an expert decision-making model to solving multi-criteria problems using the characteristic objects method

“…Decyzje podejmowane przez nich w sposób heurystyczny nie zawsze są decyzjami właściwymi (Pedrycz i in., 2011). najczęstszym powodem błędnie podejmowanych decyzji jest fakt, iż wiele problemów decyzyjnych angażuje przeciwstawne kryteria, trudne w ocenie z powodu występowania pomiędzy nimi różnego stopnia korelacji (Goodwin i in., 2009;Mosavi, 2014). wielokryterialne metody wspomagania decyzji stworzono do wspomagania rozwiązywania złożonych problemów, gdzie podejście heury-…”

Application of the fuzzy multi-criteria decision-making method to identify nonlinear decision model

“…In the complex world, the human decisions are not always correct and successful [1]. It is mostly caused by the fact that many decision problems involve a large number of conflicted objectives [2,3]. This problem relates primarily to nonlinear problems, which are more difficult to solve than linear problems [4].…”

Learning in Robotics

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