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中文摘要：
为避免算法陷入局部极值,在捕食者–猎物协同进化机制基础上,提出了一种交替捕食的粒子群优化算法（APPSO）.对该算法迭代过程进行了分析,给出并证明了粒子运动轨迹收敛的充分条件.为使粒子运动轨迹可靠收敛,构建了一种参数设置方法.通过迭代矩阵谱半径计算、SQRT序列采样,对该算法的粒子轨迹收敛速度进行了分析.基准测试函数仿真结果表明,交替捕食的PSO算法具有较佳的搜索性能.
英文摘要：
To avoid getting into local extremum,we put forward an alternately preying particle swarm optimization algorithm（APPSO） on the basis of predator-prey coevolution.The iteration process of APPSO is analyzed.The sufficient condition for the convergence of particle trajectories is proposed and proved.A parameter setting method is developed to make the particles motion trajectories reliably convergent.The convergence rate of motion trajectories in APPSO is analyzed based on the iteration matrix spectral radius and SQRT sequence.Simulation results of benchmark functions validate the correctness and efficiency of the proposed method.
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