基于改进PSO的加权直觉模糊多目标规划  

Weighted Intuitionistic Fuzzy Multi-object Programming Based on Improved Particle Swarm Algorithm

作　　者：徐小来[1] 雷英杰[1] 戴文义[1] 

机构地区：[1]空军工程大学导弹学院,三原713800

出　　处：《系统仿真学报》2009年第11期3280-3282,共3页Journal of System Simulation

基　　金：国家自然科学基金(60773209);陕西省自然科学基金(2006F18)

摘　　要：针对模糊多目标规划不区分各目标重要程度的缺陷,提出了一种加权直觉模糊多目标规划模型。首先,在模糊多目标规划的基础上,进一步定义目标函数和约束函数的非隶属函数;然后,对目标函数的隶属和非隶函数分别加权求和,通过直觉模糊"最小-最大"算子,提出了加权直觉模糊多目标规划模型,并用改进的微粒群算法求解;最后,通过一个算例表明,加权直觉模糊多目标规划的收敛速度很快,既可收敛到可行域中的最优解,又可收敛到容许偏差范围内的最优解,具有较大的实用性。A model of weighted intuitionisticfuzzy multi-objectprogramming was proposed to overcome the deficiency that fuzzy multi-object programming didn＇t differentiate important degree of each object. Firstly, on the basis of fuzzy multi-object programming, non-membership functions of object and constraint were defined. Then, membership and non-membership of object functions were summed with different weight respectively, and intuitionistic fi^zzy multi-object programming model resolved by particle swarm algorithm was proposed. At last, a typical experiment indicates that the convergence velocity is very quick, and the optimal solution is not only found infeasible region, but also found in admitted region, and the practicability is very good.

关 键 词：多目标规划 非线性规划 直觉模糊集 计算智能 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]