Optimizing Twin Support Vector Machine for Breast Cancer Classification Using a Hybrid Metaheuristic Algorithm Based on Multi-Agent Reinforcement Learning
Breast cancer diagnosis requires accurate classification methods to support early detection and improve clinical outcomes. This study aims to propose a hybrid optimization framework to enhance Twin Support Vector Machine (Twin SVM) performance for breast cancer classification. The framework integrates four marine-inspired metaheuristic algorithms — Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Marine Predators Algorithm (MPA) — as cooperative structure. Q-Learning dynamically controls the exploration-exploitation balance by adaptively selecting the most effective search operator at each iteration, preventing premature convergence and improving global search capability. Experimental results on two benchmark breast cancer datasets demonstrate that the proposed method achieves superior accuracy, precision, and recall compared to conventional Twin SVM and single-algorithm optimizers.
SolaymaniFard,O and Rahimi,A . (2026). Optimizing Twin Support Vector Machine for Breast Cancer Classification Using a Hybrid Metaheuristic Algorithm Based on Multi-Agent Reinforcement Learning. (e11745). Mathematical Research, (), e11745
MLA
SolaymaniFard,O , and Rahimi,A . "Optimizing Twin Support Vector Machine for Breast Cancer Classification Using a Hybrid Metaheuristic Algorithm Based on Multi-Agent Reinforcement Learning" .e11745 , Mathematical Research, , , 2026, e11745.
HARVARD
SolaymaniFard O, Rahimi A. (2026). 'Optimizing Twin Support Vector Machine for Breast Cancer Classification Using a Hybrid Metaheuristic Algorithm Based on Multi-Agent Reinforcement Learning', Mathematical Research, (), e11745.
CHICAGO
O SolaymaniFard and A Rahimi, "Optimizing Twin Support Vector Machine for Breast Cancer Classification Using a Hybrid Metaheuristic Algorithm Based on Multi-Agent Reinforcement Learning," Mathematical Research, (2026): e11745,
VANCOUVER
SolaymaniFard O, Rahimi A. Optimizing Twin Support Vector Machine for Breast Cancer Classification Using a Hybrid Metaheuristic Algorithm Based on Multi-Agent Reinforcement Learning. Mathematical Research. 2026;():e11745 (In Persian).
Mathematical Research