# Hybrid Genetic and Spotted Hyena Optimizer for Flow Shop Scheduling Problem
## 2 Related Works  
In recent years, swarm intelligence has been increasingly applied to solve Flow Shop Scheduling Problems (FSSP) due to its ability to effectively explore and exploit the solution space. The following studies have made significant contributions to this area:  
* [PERSON] et al., (2016) [12] proposed an energy-efficient dynamic scheduling approach for a flexible flow shop using an improved particle swarm optimization. The algorithm addresses the dynamic scheduling problem while minimizing energy consumption and makespan. * [PERSON] et al., (2021) [7] presented a discrete whale swarm algorithm for a hybrid flow-shop scheduling problem with limited buffers, considering practical constraints on buffer area resources and alternative process routes. The algorithm's effectiveness was validated on three groups of instances and a real-world industrial case. * [PERSON] et al., (2022) [13] examined the distributed assembly mixed no-idle permutation flow-shop scheduling problem (DAMNIPFSP) with a focus on minimizing total fairness. The researchers developed a mixed-integer linear programming model and proposed a Referenced Iterated Greedy (RIG) algorithm, incorporating novel destruction and reconstruction methods as well as local search methods based on a reference. Experimental results demonstrated the effectiveness of the RIG algorithm, positioning it as a state-of-the-art solution for DAMNIPFSP with the total tardiness criterion. * [PERSON] et al. (2022) [PERSON] et al. (2022) introduced a bi-objective integrated supply chain scheduling model and developed two new meta-heuristic algorithms based on multi-objective particle swarm optimization (MOPSO) to solve the strongly NP-hard flexible job shop problem. * [PERSON] et al. (2022) [PERSON] et al. (2022) investigated the impact of local search strategies on chaotic hybrid firefly particle swarm optimization algorithm in flow-shop scheduling, comparing the results of the solutions obtained using different local search strategies. * [PERSON] et al. (2022) [PERSON] et al. (2022) presented a flexible job shop scheduling problem to optimize patient flow and minimize the total carbon footprint. They developed a metaheuristic optimization algorithm called Chaotic Salp Swarm Algorithm Enhanced with Opposition-based Learning and Sine Cosine (CSSAOS) to solve this NP-hard problem
* [PERSON] et al. (2023) [PERSON] et al. (2023) explored the enhancement of Particle Swarm Optimization (PSO) in tackling Permutation Flow-Shop Scheduling Problems (PFSPs) by hybridizing it with Variable Neighborhood Search (VNS) and Simulated Annealing (SA). The authors compared the performance of the developed hybrid PSO (HPSO) algorithm with 120 distinct Taillard instances, demonstrating its robustness and significantly improved makespan optimization compared to other hybrid metaheuristics. * [PERSON] et al. (2023) [PERSON] et al. (2023) investigated production scheduling technology for knitting workshops utilizing an improved genetic algorithm (IGA) with tabu search. The research, conducted at the Key Laboratory of Modern Textile Machinery & Technology of Zhejiang Province, Zhejiang Sci-Tech University and the School of Automation, Zhejiang Institute of Mechanical & Electrical Engineering aimed to enhance production efficiency and reduce costs. Their proposed IGA demonstrated faster convergence and better search capabilities compared to traditional genetic algorithms, offering valuable insights for advancing intelligent development in knitting production. Some of the state-of-the-art optimization algorithms for FSSP include GA-SHOA, WD (2019), GA (2019), IHSA (2019), PSO (2019), CLS-BFO (2020), AGGA (2020) and SSO (2020). These algorithms were selected and used for comparison with the hybrid algorithm we propose in this paper. GA-SHOA is a new hybrid algorithm that combines the exploration capabilities of SHOA with the exploitation capabilities of GA to efficiently search the solution space.