An Improved Seagull Optimization Algorithm for the Hybrid Disassembly Line Balancing Problem with Worker Workload Consideration

This work presents an improved Multiple Objective Seagull Optimization Algorithm (MOSOA), with regular and special workers' workload considered, to solve the Hybrid Disassembly Line Balancing Problem (HDLBP). MOSOA enhances the traditional Seagull Optimization Algorithm by addressing slow convergence and local optima issues through effective search strategies, optimized parameters, and diversity mechanisms. A mathematical model of HDLBP is developed, followed by the design of the encoding and decoding mechanisms. To enhance search efficiency and solution diversity, new crossover and mutation operators, along with a ranking and crowding distance-based selection method, are introduced. Simulation experiments based on realistic parameters demonstrate the effectiveness and feasibility of MOSOA, which outperforms two traditional multiobjective optimization algorithms, NSGA-II and MOEA/D. The results highlight MOSOA's superior efficiency and potential to enhance production line stability and efficiency.