基于SQP 局部搜索的混沌粒子群优化算法

提出一种基于序贯二次规划(SQP)法的混沌粒子群优化方法(CPSO-SQP).将混沌PSO作为全局搜索器,并用SQP加速局部搜索,使得粒子能够在快速局部寻优的基础上对整个空间进行搜索,既保证了算法的收敛性,又大大增加了获得全局最优的几率.仿真结果表明,算法精度高、成功率大、全局收敛速度快,明显优于现有算法.将所提出的算法用于高密度聚乙烯(HDPE)装置串级反应过程的乙烯单耗优化,根据工业反应机理以及现场操作经验分析可知,所提出的算法是可行的.

Chaos particle swarm optimization algorithm integrated with sequential quadratic programming local search

A sequential quadratic programming integrated particle swarm optimization algorithm(CPSO-SQP) is proposed.This new algorithm uses CPSO,which makes the best of ergodicity of chaos mapping,as the global optimizer while the SQP is employed for accelerating the local search.Thus,the particles are able to search the whole space while finding local optima fast,which increases the possibility of exploring a global optimum in problems with more local optima while ensuring the convergence of algorithm.The simulation results for benchmark functions show that CPSO-SQP has better accuracy,more probability of finding global optimum and faster speed of convergence than those reported in the literature.The feasibility of the method is illustrated with the challenging ethylene piece yardage optimization problem of a cascade HDPE reaction course.