基于直觉模糊熵的粒子群多目标优化  

Particle Swarm Algorithm for Multi-objective Optimization Based on Intuitionistic Fuzzy Entropy

作　　者：苏丁为 周创明[1] 王毅[1] SU Ding-wei ZHOU Chuang-ming WANG Yi(Air and Missile Defense College, Air Force Engineering University, Xi＇an 710051, China)

机构地区：[1]空军工程大学防空反导学院,西安710051

出　　处：《计算机科学》2016年第8期262-266,共5页Computer Science

基　　金：国家自然科学基金(61402517);中国博士后基金(2013M542331);陕西省自然科学基金(2013JQ8035)资助

摘　　要：针对现有多目标算法存在的收敛性不强、分散性不高等问题,提出了一种基于直觉模糊熵的粒子群多目标优化算法(IFEMOPSO)。首先,计算出种群的直觉模糊熵(IFE),作为衡量种群在多目标空间下多样性的测度;其次,设计基于IFE的惯性权重动态变化、外部档案调用以及变异操作等3种增强算法探索力度的策略,建立了直觉模糊多目标规划模型,有效地提高了群体进化过程中的多样性,防止了算法陷入局部收敛;最后,仿真结果表明,所提算法很好地提高了所得非劣解集的收敛性和分散性,有效地解决了多目标优化问题。A particle swarm algorithm for multi-objective optimization problems based on intuitionistic fuzzy entropy was proposed to overcome the deficiency that the performance of algorithm＇s convergence and distribution is not high. Firstly, the algorithm uses a metric based on intuitionistic fuzzy entropy to measure the diversity of the population in the case of multi-objective space. Then, three strategies, namely dynamic changes of inertia weight, use of the external ar- chive and mutation operator mechanism based on intuitionistic fuzzy entropy, was designed and intuitionistic fuzzy multi- objective programming model was built to enhance the extent of the algorithm＇s exploration, increasing the diversity of the evolving population and prevent premature convergence. At last, results of simulation indicate that the proposed al- gorithm has good performance of convergence and distribution, and it is useful for dealing with multi-objective optimization problems.

关 键 词：直觉模糊熵 粒子群算法 多样性 多目标优化问题 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]