基于自适应变异粒子群算法的船舶结构优化方法

Ship structural optimization method based on daptive mutation particle swarm algorithm

[目的]由于船体结构的复杂性,传统优化方法容易出现陷入局部最优、求解速度偏慢的问题。[方法]基于自适应变异粒子群算法(AMPSO)、BP神经网络、遗传算法(GA),结合Isight/Nastran设计的正交试验方法,提出AMPSO-BP-GA结构优化方法,然后分别以十杆桁架和跳板结构的优化作为算例,验证所提优化算法的准确性和可行性。[结果]计算结果表明:在相同的约束条件下,经AMPSO-BP-GA方法优化后,十杆桁架结构重量为2272.1 kg,比其他方法优化后的结构重量更轻;跳板重量减少了33.3%,对比GA-BP-GA方法和PSOBP-GA方法分别减少25.4%和17.9%,显示AMPSO-BP-GA方法的优化效果更佳。[结论]AMPSO-BP-GA方法针对结构轻量化的优化效果更佳,可为船舶结构优化设计提供参考。

[Objectives]Due to the complexity of hull structures,traditional optimization methods are prone to fall into the local optimum and have slow solution speeds.[Methods]For this reason,based on an adaptive mutation particle swarm optimization(AMPSO)algorithm,BP neural network and genetic algorithm(GA),combined with orthogonal experiments designed by Isight/Nastran,an AMPSO-BP-GA structural optimization method is proposed.Subsequently,the optimizations of cross-bar truss and gangboard structures are used as examples to verify the accuracy and feasibility of the algorithm.[Results]The calculation results show that under the same constraints,the weight of a cross-bar truss structure optimized by the AMPSO-BP-GA method is 2272.1 kg,which is lighter than structures optimized by other methods;and using the AMPSO-BPGA method,the weight of a gangboard is reduced by 33.3%compared with the 25.4%weight reduction of the GA-BP-GA method and 17.9%weight reduction of the PSO-BP-GA method,demonstrating that the AMPSOBP-GA method has superior optimization results.[Conclusions]Compared with the three methods of BPGA,PSO-BP-GA and GA-BP-GA,the AMPSO-BP-GA method has a better effect in the optimization of lightweight structure,and can provide references for the optimization design of hull structures.