基于改进混合PSO算法的隔热材料仿真优化

针对高温环境隔热服装材料存在的质量过大等问题,通过有限元分析建立材料优化的数学模型并提出一种改进混合PSO算法,可进行仿真优化求解,用于解决高温环境材料的体积大、质量大的难题。首先从限制的边界条件出发,通过引入有限元去分析高温环境下工作服在时间和空间上的温度分布情况。为确定最优的各层材料厚度,通过建立多元非线性规划模型,运用改进混合粒子群算法进行优化求解;为了构建最优的适应度函数,引入罚函数对基本的粒子群PSO算法进行修正。为了避免盲目搜索问题,运用混合粒子群算法对全局种群更新与个体更新相联系。实际仿真数据结果表明,在上述算法求解的的高温工作服各层材料厚度较上市工作服薄1.01mm,内表恒定温度平均低0.81℃。

Simulation Optimization of High Temperature Environmental Materials Based on Improved PSO Algorithm

Based on the finite element analysis,this paper establishes the mathematical model of material optimization and proposes an improved hybrid PSO algorithm,which can be used to solve the problem of large volume and high quality of materials in high temperature environment.Starting from the limited boundary conditions,the temperature distribution of the work clothes in space and time was calculated by introducing finite element method.In order to determine the optimal thickness of each layer of materials,an improved PSO algorithm for mixed particle swarm optimization was introduced by establishing a binary nonlinear programming model.In order to build the optimal fitness function,the penalty function was introduced to correct the basic PSO algorithm.In order to avoid the problem of blind searching,the PSO algorithm was used to connect the global population update with the individual update.The actual simulation results show that the thickness of each layer of the high-temperature work clothes solved by the algorithm is 1.01 mm thinner than that of the work clothes on the market,and the average internal constant temperature is 0.81 degrees lower.