Hybrid Genetic and Spotted Hyena Optimizer for Flow Shop Scheduling Problem

This paper presents a new hybrid algorithm that combines genetic algorithms (GAs) and the optimizing spotted hyena (SHOA) to solve production shop scheduling problem. The proposed GA-SHOA incorporates operators, such as uniform crossover mutation, into SHOA improve its performance. We evaluated on set of OR library instances compared it other state-of-the-art optimization algorithms, including SSO, SCE-OBL, CLS-BFO ACGA. experimental results show consistently finds optimal or near-optimal solutions for all tested instances, outperforming algorithms. Our contributes field in several ways. First, we propose effectively exploration exploitation capabilities SHO GA, resulting balanced efficient search process finding FSSP. Second, tailor GA methods specific requirements FSSP, encoding schemes, objective function evaluation constraint handling, which ensures is well suited address challenges posed by Third, perform comprehensive performance algorithm, demonstrating effectiveness terms solution quality computational efficiency. Finally, provide an in-depth analysis behavior discussing roles components their interactions during process, can help understand factors contributing success insight potential improvements adaptations combinatorial problems.