混合混沌粒子群算法在苯与甲苯闪蒸过程优化中的应用  

Hybrid chaos particle swarm optimization algorithm and application in benzene-toluene flash vaporization

作　　者：盛煜翔[1] 潘海天[1] 夏陆岳[1] 蔡亦军[1] 孙小方[1] 

机构地区：[1]浙江工业大学化学工程与材料学院,浙江杭州310032

出　　处：《浙江工业大学学报》2010年第3期318-321,共4页Journal of Zhejiang University of Technology

摘　　要：针对粒子群算法(PSO)所存在的收敛速度慢、易陷入局部极值和优化精度较低等缺点,提出了一种自适应的混合混沌粒子群优化算法(HCPSO),根据群体适应度方差对粒子群进行自适应混沌更新.通过两种经典测试函数的寻优计算,表明HCPSO算法可显著提高寻优搜索的效率和精度.将HCPSO算法应用于苯-甲苯体系闪蒸过程的优化研究,与常规PSO算法对比,结果表明:该优化算法具有寻优效率高、全局性能好和优化结果更稳定的优点.Aiming to improve the performance a new method of adaptive hybrid chaos particle of standard particle swarm optimization （PSO） algorithm, swarm optimization （HCPSO） algorithm is introduced. During the iterative process, according to the variance of the population＇s fitness, the chaotic update of the particle is performed adaptively. Via the performance test, the validity of HCPSO algorithm was proved and then applied to solve the benzene-toluene Flash vaporization process. The results show that the amount of benzene in vapor phase product reaches the maximum on the condition of effective adjustment of temperature, pressure and current divider coefficient. Finally, the production process is optimized. By a comparison of ordinary PSO algorithm, HCPSO algorithm has higher optimization efficiency, better global performance, and more stable optimization outcomes.

关 键 词：混沌 粒子群 算法 闪蒸过程 优化 

分 类 号：TQ021.8[化学工程]