A Hybrid Group Search Optimizer with Opposition-Based Learning and Differential Evolution

被引：0

作者：

Xie, Chengwang ^{[1
]}

Chen, Wenjing ^{[1
]}

Yu, Weiwei ^{[1
]}

机构：

[1] East China Jiaotong Univ, Sch Software, Nanchang 330013, Peoples R China

来源：

COMPUTATIONAL INTELLIGENCE AND INTELLIGENT SYSTEMS, (ISICA 2015) | 2016年 / 575卷

关键词：

Group search optimizer; Opposition-based learning; Differential evolution; Hybrid group search optimizer;

D O I：

10.1007/978-981-10-0356-1_1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Group search optimizer (GSO) is a recently developed heuristic inspired by biological group search resources behavior. However, it still has some defects such as slow convergence speed and poor accuracy of solution. In order to improve the performance of GSO in solving complex optimization problems, an opposition-based learning approach (OBL) and a differential evolution method (DE) are integrated into GSO to form a hybrid GSO. In this paper, the strategy of OBL is used to enlarge the search region, and the operator of DE is utilized to enhance local search to improve. Comparison experiments have demonstrated that our hybrid GSO algorithm performed advantages over previous GSO and DE approaches in convergence speed and accuracy of solution.

引用

页码：3 / 12

页数：10

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