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Due to rising environmental concerns, green innovation has become a familiar and appealing topic worldwide in recent years. In addition, population growth, globalization, urbanization, and industrialization have given rise to many problems, such as damage to the environment, the economy, and the living conditions of society. This paper aims to evaluate and prioritize aspects of green innovation, taking into account sustainability performance indicators. FUCOM-MARCOS hybrid methods were used. The experimental results of the proposed method showed that management technological innovation (C1) is the most influential part for adopting green practices in the textile industry in Nigeria. The study also showed that greening the supplier (C6) and product technology innovation (C5) are the second and third most important aspects of green innovation. Furthermore, it analyzed the sustainability performance indicators using the MARCOS method. The findings reveal that social performance (SPI-3) was the most sustainable and vital indicator in terms of green innovation practices in the textile sector in Nigeria. Sensitivity analysis was also conducted using five other methods, and the results obtained showed stability in the order of the indicators.

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Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. Generally, clustering is difficult and complex phenomenon, where the appropriate numbers of clusters are always unknown, comes with a large number of potential solutions, and as well the datasets are unsupervised. These problems can be addressed by the Multi-Objective Particle Swarm Optimization (MOPSO) approach, which is commonly used in addressing optimization problems. However, MOPSO algorithm produces a group of non-dominated solutions which make the selection of an “appropriate” Pareto optimal or non-dominated solution more difficult. According to the literature, crowding distance is one of the most efficient algorithms that was developed based on density measures to treat the problem of selection mechanism for archive updates. In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. The approach is based on the dominance concept and crowding distances mechanism to guarantee survival of the best solution. Furthermore, we used the Pareto dominance concept after calculating the value of crowding degree for each solution. The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. The results of the evaluation show that the proposed approach exemplified the state-of-the-art method with significant differences in most of the datasets tested.

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