IPSO算法在舰用设备抗冲击能力分析中的应用

Application of the IPSO Algorithm in Analysing the Shock Resistance of the Equipment Used on Naval Ships

本文提出了一种能够保证以概率1收敛于全局最优解的改进粒子群优化(IPSO)算法。算法在运行过程中根据粒子的浓度和趋同性函数来确定当前粒子的变异概率,增强了粒子群优化算法跳出局部最优的能力。同时,引入的自适应加速度系数,更好地协调全局和局部搜索能力,有利于快速找到全局最优点。将其应用于典型设备抗冲击能力分析研究,结果表明,IPSO算法搜索能力有了显著提高,应用于设备抗冲击研究能提高计算的精确度,降低预测误差。

A new improved particle swarm optimization （IPSO） algorithm, which guarantees to converge to the global optimization solution with probability one is proposed. During the running time, the mutation probability for the current particle is determined by the variance of the individual＇s concentration and convergence function. The ability of IPSO to break away from the local optimum is greatly improved by the mutation. The concept of adaptive acceleration factor is introduced to the IPSO. In this manner, the global and local search capability can be coordinated to make for locating the global opti mum quickly. Finally, IPSO is applied to optimizing several test functions and shock resistance of equipment research. Test results show that IPSO has great advantage of the convergence property, and the shock resistance of equipment research shows that IPSO is effective.