Development of an Investment Sector Selector Using a TOPSIS Method Based on Novel Distances and Similarity Measures for Picture Fuzzy Hypersoft Sets

• Published in: IEEE access 2024, Vol.12, p.45118-45133
• Main Authors: Imran Harl, Muhammad, Saeed, Muhammad, Saeed, Muhammad Haris, Bajri, Sanaa Ahmed, Alburaikan, Alhanouf, Abd El-Wahed Khalifa, Hamiden
• Format: Article
• Language: English
• Subjects: algorithms; Alternatives; Biological system modeling; Complexity; Current measurement; Data structures; Decision analysis; Decision making; Economic development; Financial management; Fuzzy set theory; Fuzzy sets; hypersoft set; Investment; Multiple criterion; optimization; picture fuzzy hypersoft set; Similarity; similarity measure; Similarity measures; Soft set; TOPSIS; Uncertainty
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3380025

The selection of an optimal investment sector is of critical importance not only for individual financial success but also to drive economic development. The allocation of capital into sectors with high potential for growth, innovation, and job creation is key. In addressing the complexity of decision-making scenarios associated with investment sector exploration, we introduce a novel data structure known as Picture fuzzy hypersoft set ( \mathbb {PF}_{\mathbb {HSS}s} ). This specialized approach within computational intelligence and decision-making aims to categorize data into various attributes and sub-attributes, considering the significant role of neutrality. The study stems from the need for a comprehensive framework ( \mathbb {PF}{\mathbb {HSS}s} ) that can effectively handle intricate decision-making scenarios involving attributes, subattributes, and nuanced factors such as neutrality. Traditional tools such as TOPSIS and its extensions of fuzzy sets, while robust in Multiple Criteria Decision Making (MCDM), may face challenges in modeling and analyzing decision-making information within a \mathbb {PF}{\mathbb {HSS}s} environment. The rationale behind this study lies in enhancing the accuracy and efficiency of decision-making processes when dealing with complex, fuzzy, and multi-criteria data. By introducing newly proposed distances and similarity measures tailored to \mathbb {PF}{\mathbb {HSS}s} , and constructing a \mathbb {PF}{\mathbb {HSS}s} -TOPSIS method, we aim to address the limitations faced by existing models in the \mathbb {PF}_{\mathbb {HSS}s} environment. The application of Hamming distance-based similarity measures further distinguishes our method by determining the weights assigned to each decision maker. The proposed \mathbb {PF}_{\mathbb {HSS}s} -TOPSIS method is practically applied in designing an optimal investment sector exploration tool for investors. This method has the potential to establish a crucial connection between alternatives