A novel approach using Dynamic Social Impact Theory for optimization of impedance-Tongue (iTongue)

This paper presents a novel multiobjective wrapper approach using Dynamic Social Impact Theory based optimizer (SITO). A Fuzzy Inference System in conjunction with support vector machines classifier has been used for the optimization of an impedance-Tongue for the classification of samples collected from single batch production of Kangra orthodox black tea. Impedance spectra of the tea samples have been measured in the range of 20 Hz to 1 MHz using a two electrode setup employing platinum and gold electrodes. The proposed approach has been compared, for its robustness and validity using various intra and inter measures, against Genetic Algorithm and binary Particle Swarm Optimization. Feature subset selection methods based on the first and second order statistics have also been employed for comparisons. The proposed approach outperforms the Genetic Algorithm and binary Particle Swarm Optimization.

► Optimization of an electronic tongue. ► Dynamic Social Impact Theory as an optimizer (SITO). ► A novel wrapper approach for feature selection. ► Using Electrochemical Impedance Spectroscopy for tea classification. ► Comparison of SITO with GA and PSO.