OICC Press Fuzzy Optimization and Modeling Journal (FOMJ) 2676-7007 7 1 2026 03 30 An ABS-GA Algorithm for Solving Fuzzy Optimization Problems 10.57647/fomj.2026.0701.04 EN Ali Mehrabian Faculty of Mathematical Sciences, Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran https://orcid.org/0009-0008-3649-984X Reza Ghanbari Faculty of Mathematical Sciences, Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran Khatere Ghorbani-Moghadam Mosaheb Institute of Mathematics, Kharazmi University, Tehran, Iran Journal Article 2026 03 30 This paper presents a fuzzy programming model with LR fuzzy coefficients. To solve it efficiently, we propose a novel hybrid ABS-GA algorithm that synergistically combines the ABS algorithm for dimensionality reduction with a Genetic Algorithm (GA). First, ABS projects the original n-dimensional problem into a reduced (n−m)-dimensional subspace using the linear constraints Ax = b, ensuring feasibility and shrinking the search space. Then, a tailored GA optimizes within this reduced space, employing Ghanbari et al. [1] O(1) comparison formula for direct and efficient fuzzy number evaluation, and a novel tangent cone-based mutation operator for enhanced local exploration. Numerical experiments demonstrate that ABS-GA significantly outperforms existing methods in both solution quality and computational efficiency, validating the effectiveness of the integrated approach.