樊永生,熊焰明,余红英

• 1:中北大学大数据学院
• 2:中北大学电气与控制工程学院

摘要(Abstract)：

针对求解支持向量机反问题的效率较低,算法复杂度高以及运用传统方法求解该问题容易陷入局部最优出现早熟收敛的问题,提出一种基于改进差异的差异演化算法。该算法在标准差异演化算法的基础上利用种群分类机制对算法进行改进,对改进后的算法与标准差异演化算法和K-means聚类算法进行实验设计,并对算法最终实验结果进行分析,改进的差异演化算法除在运行时间外,结果对比以及最大间隔次数比都有明显的提升,有效地保护处于最优解区域但是适应值低的个体,能够提高算法局部搜索能力,有助于算法实现全局收敛。实验结果表明,改进的差异演化算法在求解SVM反问题上能有明显的提升。

关键词(KeyWords)： 支持向量机;局部最优;差异演化算法;全局收敛;种群分类机制;IRIS数据库

Abstract:

Keywords:

基金项目(Foundation): 山西省自然科学基金(201601D102029)~~

作者(Author): 樊永生,熊焰明,余红英

DOI: 10.16652/j.issn.1004-373x.2018.06.034

参考文献(References)：

• [1]GE Y,LI X X,LANG L H,et al.Optimized design of tube hydroforming loading path using multi-objective differential evolution[J].International journal of advanced manufacturing technology,2017,88(4):837-846.
• [2]YANG Z,RAUEN Z I,LIU C,et al.Automatic tuning on many-core platform for energy efficiency via support vector machine enhanced differential evolution[J].Scalable computing:practice and experience,2017,18(2):117-132.
• [3]WANG G F,XIE Q L,ZHANG Y C.Tool condition monitoring system based on support vector machine and differential evolution optimization[J].Proceedings of the institution of mechanical engineers,part B:journal of engineering manufacture,2017,231(5):805-813.
• [4]YU X,WANG X.A novel hybrid classification framework using SVM and differential evolution[J].Soft computing,2016,21(14):4029-4044.
• [5]YU W J,SHEN M,CHEN W N,et al.Differential evolution with two-level parameter adaptation[J].IEEE transactions on cybernetics,2014,44(7):1080-1099.
• [6]JOSE-GARCIA A,GOMEZ-FLORES W.Automatic clustering using nature-inspired metaheuristics:a survey[J].Applied soft computing,2016,41:192-213.
• [7]LI G H,LIN Q Z,CUI L Z,et al.A novel hybrid differential evolution algorithm with modified Co DE and JADE[J].Applied soft computing,2016,47:577-599.
• [8]CUI L Z,LI G H,LIN Q Z,et al.Adaptive differential evolution algorithm with novel mutation strategies in multiple subpopulations[J].Computers&operations research,2016,67:155-173.
• [9]LIN Q Z,ZHU Q L,HUANG P Z,et al.A novel hybrid multiobjective immune algorithm with adaptive differential evolution[J].Computers&operations research,2015,62:95-111.
• [10]KOVA?EVI?D,MLADENOVI?N,PETROVI?B,et al.DE-VNS:self-adaptive differential evolution with crossover neighborhood search for continuous global optimization[J].Computers&operations research,2014,52:157-169.
• [11]YI W,GAO L,LI X,et al.A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems[J].Applied intelligence,2015,42(4):642-660.
• [12]LI Y L,ZHAN Z H,GONG Y J,et al.Fast micro-differential evolution for topological active net optimization[J].IEEE transactions on cybernetics,2016,46(6):1411-1423.
• [13]YILDIZ A R.Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations[J].Applied soft computing,2013,13(3):1433-1439.
• [14]TSAI J T,FANG J C,CHOU J H.Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm[J].Computers&operations research,2013,40(12):3045-3055.
• [15]QIN A K,HUANG V L,SUGANTHAN P N.Differential evolution algorithm with strategy adaptation for global numerical optimization[J].IEEE transactions on evolutionary computation,2009,13(2):398-417.
• [16]SUGANTHI S T,DEVARAJ D,RAMAR K,et al.An improved differential evolution algorithm for congestion management in the presence of wind turbine generators[J].Renewable and sustainable energy reviews,2018,81:635-642.