VIII. Academic Studies Congress | Andizhan, Uzbekistan

A Hybrid Bat-Bee Colony Algorithm for Flowshop Scheduling Optimization

Not scheduled

20m

Description

Flowshop scheduling problems necessitate optimizing job sequences on a limited number of machines. This research explores a novel hybrid approach by integrating the strengths of the Bat Algorithm (BA) and the Artificial Bee Colony (ABC) algorithm for flowshop scheduling optimization. The BA, inspired by echolocation of bats, excels at global exploration, while the ABC algorithm, mimicking bee foraging behavior, is adept at local exploitation. However, both algorithms can be susceptible to limitations. This hybrid approach aims to leverage the complementary strengths of these algorithms. The BA will be employed for its global search capabilities to identify diverse initial solutions in the flowshop scheduling space. Subsequently, the ABC algorithm will be utilized for its local search proficiency to refine these solutions and converge towards optimal schedules. The objective will be to minimize the makespan (completion time of the last job). The proposed hybrid Bat-Bee Colony (BB-ABC) algorithm will be evaluated on benchmark flowshop scheduling problems. The performance will be compared against the standalone BA, ABC algorithm, and other existing flowshop scheduling methods. We anticipate that this integration will lead to superior performance by effectively combining global exploration and local exploitation, resulting in significantly reduced makespan compared to existing approaches.