基于MOSOA的机器人协同区域搜索OA

To solve the multi-objective optimization problem in robot cooperative area search,this paper proposed a multi-objective seagull optimization algorithm(MOSOA),and verified its performance.The algorithm included a neighborhood crowding degree-based objective selection mechanism,along with a dynamic archive size adjustment strategy and an adaptive archive update rule.It proved that the Pareto optimal solution sequence generated by MOSOA converges to the true Pareto opti-mal front with probability 1.It used two types of multi-objective test functions to verify the basic performance of the algorithm,which was then applied to robot cooperative area search tasks.Results show that compared with the MOPSO,MOSOA reduced the path length standard deviation by approximately 40.1％,shortened the average path length by 12％,and increased the ave-rage coverage rate by 3.2％.The results show that MOSOA performs well in terms of theoretical convergence,solution quali-ty,and practical engineering applicability,and is particularly suitable for multi-objective optimization scenarios such as robot path planning.