基于改进的差异演化算法求解SVM反问题的研究
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摘要
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。
Since the traditional algorithm has the problems of low efficiency of support vector machine(SVM)inverse problem solving and high algorithm complexity,is easily to fall into local optimum,and is prone to the premature convergence,a new differential evolutionary algorithm based on improved difference is proposed. On the basis of normative differential evolution algorithm,the population classification mechanism is used to improve the algorithm. The experimental design was carried out for the improved algorithm,normative differential evolution algorithm and K-means clustering algorithm. The final experimental results of the algorithm are analyzed. The maximum interval numbers and average interval numbers of the changed differential evolution(CDE)algorithm beyond operation time are improved greatly. The algorithm can effectively protect the individual within the optimum solution region but with low adaptive value,improve the local search ability of the algorithm,and is conductive to the realization of global convergence. The experimental results show that the performance of the CDE algorithm is improved obviously for SVM inverse problem solving.
Since the traditional algorithm has the problems of low efficiency of support vector machine(SVM)inverse problem solving and high algorithm complexity,is easily to fall into local optimum,and is prone to the premature convergence,a new differential evolutionary algorithm based on improved difference is proposed. On the basis of normative differential evolution algorithm,the population classification mechanism is used to improve the algorithm. The experimental design was carried out for the improved algorithm,normative differential evolution algorithm and K-means clustering algorithm. The final experimental results of the algorithm are analyzed. The maximum interval numbers and average interval numbers of the changed differential evolution(CDE)algorithm beyond operation time are improved greatly. The algorithm can effectively protect the individual within the optimum solution region but with low adaptive value,improve the local search ability of the algorithm,and is conductive to the realization of global convergence. The experimental results show that the performance of the CDE algorithm is improved obviously for SVM inverse problem solving.
引文
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[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.