Solution Techniques for Fuzzy Graph Partitioning Based on Heuristic Optimization

Alsaeedi, Mohammad; Tavakolli, Mostafa; Abouyee, Ahmad; Ghorbani Moghadam, Khatere; Ghanbari, Reza

doi:10.30473/coam.2025.74009.1296

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	Solution Techniques for Fuzzy Graph Partitioning Based on Heuristic Optimization
Control and Optimization in Applied Mathematics
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 تیر 1404 اصل مقاله (664.48 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.30473/coam.2025.74009.1296
نویسندگان
Mohammad Alsaeedi¹؛ Mostafa Tavakolli^* ¹؛ Ahmad Abouyee¹؛ Khatere Ghorbani Moghadam²؛ Reza Ghanbari¹
¹Faculty of Mathematical Sciences‎, ‎Department of Applied Mathematics‎, ‎Ferdowsi University of Mashhad‎, ‎Mashhad‎, ‎Iran.
²Mosaheb Institute of Mathematics‎, ‎Kharazmi University‎, ‎Tehran‎, ‎Iran‎.
چکیده
In this study‎, ‎we proposed a novel graph partitioning problem where the edges are characterized by trapezoidal fuzzy numbers‎. ‎A linear ranking function is employed to establish an order among these fuzzy numbers‎. ‎We derive the necessary conditions for the existence of an optimal solution to this problem‎. ‎To address the fuzzy graph partitioning problem‎, ‎we implement and compare the performance of three algorithms: Genetic Algorithm‎, ‎Tabu Search, and Sequential Least Squares Programming‎. ‎ The algorithms are evaluated based on objective values‎, ‎computational time‎, ‎and the number of iterations across multiple numerical examples‎. ‎Utilizing Dolan-Moré performance profiles‎, ‎we demonstrate the superiority of our proposed approach relative to existing methods‎. ‎The findings highlight the robustness and computational efficiency of our methodology, making a meaningful contribution to the advancement of fuzzy graph algorithms and their practical applications.
تازه های تحقیق
Formulated a novel graph partitioning problem where the edges are represented by trapezoidal fuzzy numbers. Applied a linear ranking function to systematically and effectively order the trapezoidal fuzzy numbers, facilitating decision-making and optimization. Established necessary conditions that must be satisfied for the existence of an optimal solution to the fuzzy GPP, providing theoretical insights into the problem's solvability. Implemented and compared three distinct optimization techniques, Genetic Algorithm, Tabu Search, and Sequential Least Squares Programming, to solve the fuzzy GPP efficiently. Conducted extensive numerical testing to evaluate each approach based on objective function value, computational time, and iteration number across multiple instances. Employed Dolan-Moré performance profiles to visually demonstrate the efficiency and robustness, and reliability of the proposed approach.
کلیدواژه‌ها
‎Fuzzy؛ Fuzzy Graph-partitioning‎؛ Fuzzy graph؛ Heuristic optimization؛ Fuzzy edge representation

مراجع
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Solution Techniques for Fuzzy Graph Partitioning Based on Heuristic Optimization