在求解高维空间中复杂多峰函数的实时优化问题时,传统的粒子群算法在收敛速度和局部搜索能力等方面表现出严重不足,针对这些问题,启发于鲦鱼效应的生物现象,引入团队领导机制,提出基于多leader交叉的PSO算法MLCPSO,该算法集成了两种新的粒子飞行策略.实验表明,从实验结果的平均情形上看,与SGA算法与SPSO算法相比较,MLCPSO算法具有更优的收敛性与扩展性. When solving the real-time optimization problem of complex multi-peaked functions in high-dimensional space, the traditional particle swarm optimization algorithm shows a serious shortage of convergence speed and local search ability. In response to these problems, the biological phenomenon inspired by the anchovy effect is introduced into the team The paper proposes two new PSO algorithms based on multi-leaders cross MLCPSO, which incorporates two new particle flight strategies.Experiments show that compared with the SGA algorithm and the SPSO algorithm, the MLCPSO algorithm has more Excellent convergence and expansion.