A Multi-Strategy Enhanced Marine Predator Algorithm for Global Optimization and UAV Swarm Path Planning

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, the path planning problem in UAV cooperative operations has become increasingly prominent. This paper studies the path planning problem of UAV swarms, aiming to propose an effective method to improve the efficiency and safety of task execution. The Marine Predator Algorithm (MPA) is a popular optimization algorithm known for its high performance. However, it sometimes encounters issues such as low efficiency in spiral flight and slow predator speed, leading to slow convergence and susceptibility to local optima. To address these limitations, this paper proposes the Multi-strategy Enhanced Marine Predator Algorithm (MEMPA). The four enhancement strategies are: random spiral flight strategy, crisscross strategy, centroid boundary control strategy, and improved update rules for eddy formation and the FADs’ effect. These strategies collectively enhance the algorithm’s global search ability and its capacity to escape local optima. Compared with FDB-PPSO, AGWO, HPHHO, LSHADE, LSHADE-cnEpSin, MadDE, NMPA, QQLMPA, and the original MPA across four different dimensions of the 30 CEC2017 test functions, the Wilcoxon rank sum test results show that MEMPA outperforms the original MPA by 21, 19, 22, and 25 on the four dimensions, respectively. According to the results of the Friedman mean rank test, the top three algorithms are LSHADE, MPA, and MEMPA, with scores of 2.91, 2.77, and 2.14, respectively. Additionally, the proposed MEMPA was used to solve the UAV swarm path planning problem. The results show that the solutions obtained by MEMPA are 10%, 10%, and 13% better than those obtained by MPA, NMPA, and QQLMPA, respectively. It is superior to the selected comparison algorithms in terms of solution effectiveness and stability, demonstrating its potential in practical applications.