A Hybrid Migratory Bird Optimization Algorithm for solving the Large-Scale Flow Shop Scheduling Problem

The flow-shop scheduling problem (FSP) is significant in modern industry, with increasing complexity and computation time as the scheduling scale grows. To address this, this paper integrates the Migratory Bird Optimization (MBO) algorithm with the Simulated Annealing (SA) algorithm. This hybrid approach leverages MBO's robust global search and SA's effectiveness in avoiding local optima. The hybrid MBO algorithm aims to minimize makespan, supporting automated, intelligent, and unmanned production in core workshops. Parts of the Taillard benchmark are used to verify the proposed SAMBO algorithm's performance, comparing it with the memetic and hybrid genetic algorithms. Experimental results show that SAMBO better saves processing time.