Improvement of the Seagull Optimization Algorithm and Its Application in Path Planning

Seagull Optimization Algorithm (SOA) is an emerging intelligent optimization algorithm proposed in recent years. This paper proposes an improvement in the SOA based on Levy flight(LSOA), which aims to solve the problems of decreasing exploration ability and easing to fall into local extreme values in the late stage of SOA. LSOA could increase the diversity of the seagull population, improve the exploration ability of SOA. Then selecting the classic 23 benchmark functions for simulation experiments and comparing with the other five intelligent algorithms in recent years. The result shows that LSOA has improved stability, convergence speed, and search accuracy, effectively avoiding local optimum conditions, with broad research prospects. Finally, LSOA is applied to the path planning problem, proving the algorithm’s feasibility and having a good optimization effect.