基于人工鱼群算法的桁架结构的优化

针对基本人工鱼群算法在解决桁架结构优化问题时存在后期收敛速度慢、寻优精度不高的缺陷，在算法初期利用混沌运动遍历性、随机性等特点初始化解群，提高求解效率和解的质量，在算法运行过程中利用粒子群优化算法惯性权重调整策略对人工鱼的步长进行改进，提高寻优的速度和精度。将改进后的算法应用到桁架结构优化中，以桁架截面尺寸为设计变量，结构重量最小为目标函数建立优化设计模型，运用Matlab进行模型优化分析，并与其它算法优化结果进行对比。结果表明，改进的算法在收敛速度与寻优精度方面均有所提高，尤其在迭代计算的初期，效果非常明显，迭代次数为55次左右时优化结果基本平稳。

Improved artificial fish the structuralswarm algorithm and its application tooptimum design of the truss

Artificial fish-swarm algorithm(AFSA) has the disadvantages of slow convergence and low accuracy for the optimal design of truss structures. At the initialization stage of algorithm, the transverse and randomness of chaos was introduced to initialize fish group, so that the efficiency and quality of solution could be improved. During the running time, the inertia weight adjustment strategy of particle swarm optimization was used to adjust the artificial fish's vision and step, then the speed and accuracy of optimization could be raised.The improved artificial fish swarm algorithm was applied to the optimal design of truss structures.The model of optimal design was established and the Matlab was used to analyse it.The results were compared with other algorithms and showed that the convergence speed and precision of improved algorithm were improved, especially in the early iterations, the effect was very obvious and the number of iterations for the optimization results were stable about 55 times.