【作者】 汪丽娜；

【导师】 曹萃文；

【作者基本信息】 华东理工大学 ， 控制科学与工程， 2012， 硕士

【摘要】 文化算法是一种受社会进化启发而提出的双层进化模型,下层种群空间有机结合任一智能优化算法,上层信念空间通过获取种群空间的进化信息或经验来指导种群空间的进化,从而提高算法优化性能。本文将和声搜索算法和粒子群优化算法分别纳入到文化算法的框架,提出了一种文化和声搜索算法和一种改进的文化粒子群优化算法,两种算法均能有效地求解约束优化问题；以炼油厂实际生产过程为背景,将改进的文化粒子群优化算法应用于调合组分不确定的多组分石脑油调合优化问题中,仿真结果验证了模型和算法的有效性。本文主要工作如下：(1)概述了粒子群优化算法与和声搜索算法的起源、发展历程、特点及应用,重点介绍了优于传统智能优化算法的文化算法,包括其来源、计算框架、特点和应用领域,并详述了如何设计和实现文化算法。从工业应用角度出发,对油品调合问题和石脑油的国内外研究现状进行了综述。(2)提出了一种文化和声搜索算法。该算法以文化算法的双层进化结构为框架,将和声搜索算法纳入到文化算法的下层种群空间中；上层信念空间从种群空间的和声记忆库提取优秀和声隐含的进化知识,对种群空间的较差和声进行微调,指导种群空间更好地进化,增加了和声记忆库的多样性,提高了和声搜索算法的全局寻优能力。对多个典型约束函数的仿真结果表明了该算法具有良好的优化性能。(3)提出了一种改进的文化粒子群优化算法。该算法仍以文化算法的双层进化结构为框架,下层种群空间的进化采用粒子群优化算法；上层信念空间将种群空间的部分最优粒子作为信念空间的精英种群,更新知识库并继续新的粒子群优化操作,信念空间根据精英种群产生的优秀粒子再次更新知识库,这样既能保证种群多样性,也能加快知识的更新速度,从而更有效地指导种群空间的进化。对多个典型约束函数进行了仿真,仿真结果表明该算法较大程度上提高了文化粒子群优化算法的全局搜索能力和收敛速度。(4)针对某炼油厂调合组分不确定的石脑油实际生产调合问题,建立一种基于组合数学的石脑油调合约束优化模型,采用(3)中改进的文化粒子群优化算法进行求解,在满足调合后石脑油的质量属性约束及质量属性约束的前提下,仿真获得了满足生产需要的5种最优配方,仿真结果验证了算法和模型的有效性。更多还原

【Abstract】 Cultural algorithm (CA) is a dual inheritance system that models evolution in human society at both the macro-evolutionary level and the micro-evolutionary level, which correspond to belief space and population space. Any intelligent optimization algorithm can be integrated into the population space and induced by the belief space which can abstract evolutionary information or experience from population space. In this way, the performance of these algorithms can be improved. In this dissertation, harmony search algorithm (HS) and particle swarm optimization (PSO) algorithm are embedded into the framework of CA. Two new algorithms (a CA based on harmony search algorithm, HSCA; an improved particle swarm optimization algorithm based on cultural algorithm, IPSOCA) are proposed which can solve constrained optimization problems effectively. In a real-world refinery, IPSOCA is applied in a type of component-uncertain naphtha blending optimization problem. The contents are summarized as follows.(1) The origin, development and application of the existing HS and PSO are first introduced. Then, CA is presented in detail which including its origin, framework, characteristics, applications, and the designing method. Finally, the development of oil blending optimization methods and general researches on naphtha are reviewed.(2) In order to improve the global search capability of HS, a HSCA algorithm is proposed in this paper. The algorithm embeds HS into the lower layer (population space) of CA framework, the upper layer (belief space) extracts evolution knowledge from perfect harmony in population space and in return makes full use of the knowledge to guide the evolution of population space, such as adjustment in some harmony of bad state in population space. In this way, it ensures the diversity of population and speeds up the evolutionary process. Simulation experiments of several classical constrained functions show that HSCA performs better than HS. (3) An IPSOCA algorithm is developed to solve constrained optimization problems. The algorithm takes advantage of the dual inheritance framework of CA, the particles in population space evolves with an improved PSO algorithm, fixed proportion elite particles are selected from population space to construct the basic elite-swarm and update evolution knowledge again in belief space. After that, the belief space passes down the new knowledge which has been updated twice to give better and further guidance to all the particles in the population space. In this way, it not only improves the diversity of population but also increases the speed of updating knowledge. Simulation results verify the advantages of IPSOCA in converging speed, precision and global searching ability.(4) A type of uncertain-component naphtha blending optimization problem is considered. A blending recipe optimization model based on combinatorial mathematics and constrained optimization is firstly constructed. IPSOCA is then used to solve the model and obtain the best recipe. The simulation results suggest the feasibility of the optimization and the validity of IPSOCA again.更多还原