MODIFIED GRAVITATIONAL SEARCH ALGORITHM WITH PARTICLE MEMORY ABILITY AND ITS APPLICATION

被引：0

作者：

Gu, Binjie ^{[1
]}

Pan, Feng ^{[1
]}

机构：

[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, 1800 Lihu Rd, Wuxi 214122, Peoples R China

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2013年 / 9卷 / 11期

关键词：

Gravitational search algorithm; Particle swarm optimization; Particle memory ability; Benchmark function; Support vector machine classification;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Gravitational search algorithm (GSA) is a type of optimization algorithm based on the law of gravity and mass interactions, which is lacking of memory ability. To enhance particle memory ability and search accuracy of GSA, a modified GSA (MGSA) is developed. MGSA adopts the idea of local optimum solution and global optimum solution from particle swarm optimization (PSO) algorithm into GSA. Furthermore, the convergence property of MGSA is analyzed. The performance of MGSA has been evaluated on 12 standard benchmark functions, and the results were compared with GSA. The obtained experimental results verified the effectiveness of MGSA in solving high dimensional benchmark functions. Additionally, to test MGSA performance in practical issue, MGSA is applied into support vector machine (SVM) parameter settings, the results showed that suitable SVM parameters could be effectively found by MGSA.

引用

页码：4531 / 4544

页数：14

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