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The production rates of textile materials increase day by day due to the increasing population of the world. This rapid increase in production boosts the demand of raw materials to be used in textile manufacturing. The insufficiency of necessary natural fiber supply leads to new investigations of other natural fiber alternatives since the use of sustainable and inexpensive natural fibers presents an opportunity to protect the environment. Coir fiber obtained from the husk of ripe/matured coconut has various fields of use such as door mats, floor mats, brushes, matresses, upholstery padding, sacking and horticulture. In this study, in order to determine the optimum bleaching conditions for coir fiber, a wide variety of peracetic acid concentrations are applied at different bleaching processing times. Color strength values of bleached coir fiber fabrics vary under different conditions. This study aims to obtain the estimation of color strength values on the samples of coir fibers bleached with peracetic acid. In order to solve the problem, various mathematical function models are utilized. Then, an artificial bee colony algorithm is used to optimize the weights of the functions. Sample data of the fiber is used to verify the correctness of the color strength estimation model. The estimated results are compared with the results of experimental studies. Results show that the prediction model can fit the experimental results with a high precision. The model based on artificial bee colony is a good candidate to estimate the color strength of bleached coir fiber fabric.

We introduce a new framework that leverages machine learning models known as generative models to solve optimization problems. Our Generator-Enhanced Optimization (GEO) strategy is flexible to adopt any generative model, from quantum to quantum-inspired or classical, such as Generative Adversarial Networks, Variational Autoencoders, or Quantum Circuit Born Machines, to name a few. Here, we focus on a quantum-inspired version of GEO relying on tensor-network Born machines and referred to hereafter as TN-GEO. We present two prominent strategies for using TN-GEO. The first uses data points previously evaluated by any quantum or classical optimizer, and we show how TN-GEO improves the performance of the classical solver as a standalone strategy in hard-to-solve instances. The second strategy uses TN-GEO as a standalone solver, i.e., when no previous observations are available. Here, we show its superior performance when the goal is to find the best minimum given a fixed budget for the number of function calls. This might be ideal in situations where the cost function evaluation can be very expensive. To illustrate our results, we run these benchmarks in the context of the portfolio optimization problem by constructing instances from the S\&P 500 and several other financial stock indexes. We also comprehensively compare state-of-the-art algorithms in a generalized version of the portfolio optimization problem. The results show that TN-GEO is among the best compared to these state-of-the-art algorithms; a remarkable outcome given the solvers used in the comparison have been fine-tuned for decades in this real-world industrial application. We see this as an important step toward a practical advantage with quantum-inspired models and, subsequently, with quantum generative models., Significantly revised since v1. Main revisions: A thorough comparison of our quantum-inspired optimization strategy with state-of-the-art optimizers. 15 pages, 6 figures