How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic

Tania Pencheva,Maria Angelova,Evdokia Sotirova,Krassimir Atanassov

doi:10.3390/math9182189

Abstract

Intuitionistic fuzzy logic is the main tool in the recently developed step-wise “cross-evaluation” procedure that aims at the assessment of different optimization algorithms. In this investigation, the procedure previously applied to compare the effectiveness of two or three algorithms has been significantly upgraded to evaluate the performance of a set of four algorithms. For the first time, the procedure applied here has been tested in the evaluation of the effectiveness of genetic algorithms (GAs), which are proven as very promising and successful optimization techniques for solving hard non-linear optimization tasks. As a case study exemplified with the parameter identification of a S. cerevisiae fed-batch fermentation process model, the cross-evaluation procedure has been executed to compare four different types of GAs, and more specifically, multi-population genetic algorithms (MGAs), which differ in the order of application of the three genetic operators: Selection, crossover and mutation. The results obtained from the implementation of the upgraded intuitionistic fuzzy logic-based procedure for MGA performance assessment have been analyzed, and the standard MGA has been outlined as the fastest and most reliable one among the four investigated algorithms.

Highlights

IntroductionModel parameter identification of non-linear fermentation processes (FP) is an important step for adequate modeling
Intuitionistic fuzzy logic has been applied as a main tool when assessing the quality of different algorithms performance for parameter estimation of a fed-batch fermentation process model
Aiming to retain the promising results achieved in previous legs of this research, namely less convergence time at preserved model accuracy, the assess the algorithm quality performance (AAQP) procedure overbuilds the results obtained after the application of the recently developed purposeful model parameters genesis (PMPG) procedure

Summary

Introduction

Model parameter identification of non-linear fermentation processes (FP) is an important step for adequate modeling. Conventional optimization methods fail and do not lead to a satisfactory solution [1]. Stochastic algorithms appear as a reliable alternative. Genetic algorithms (GAs) [2], based on Darwin’s theory of evolution and “survival of the fittest” concept, are a stochastic technique for global optimization broadly applied to various complicated problems in different areas [3]. GAs find the global optimal solution by simultaneously evaluating multiple points in the parameter search space. Properties of GAs like noise tolerance, easy interface interaction and hybridization make them a suitable and reliable tool to handle hard problems like FP parameter identification [3,4,5]

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Conclusion