Parameters optimization of fabric finishing system of a textile industry using teaching–learning-based optimization algorithm

Abstract

In the present work, a recently developed advanced optimization algorithm named as teaching-learning-based optimization (TLBO) is used for the parameters optimization of fabric finishing system of a textile industry. Fabric Finishing System has four main subsystems, arranged in hybrid configuration. For performance modeling and analysis of availability, a performance evaluating model of fabric finishing system has been developed with the help of mathematical formulation based on Markov-Birth-Death process using Probabilistic Approach. Then, the overall performance of the concerned system has first analyzed and then, optimized by using teaching-learning-based optimization (TLBO). The results of optimization using the proposed algorithm are validated by comparing with those obtained by using the genetic algorithm (GA) on the same system. Improvement in the results is obtained by the proposed algorithm. The results of effect of variation of the algorithm parameters on fitness values of the objective function are reported.

Stochastic Modelling of Pilling Degree Changes During the Pilling Process of Wool Fabrics

“…A performance evaluating model of the system was obtained based on Markov-Birth-Death process with probabilistic method. The teaching-learning-based optimization (TLBO) was used as the optimization algorithm [15]. Baycan and Yildirim (2016) investigated the cyclical economic asymmetric behavioural dynamics of the Turkish textile and apparel industries.…”