Multiple Trajectory Search for Large Scale Global Optimization

被引：194

作者：

Tseng, Lin-Yu ^{[1
]}

Chen, Chun ^{[2
]}

机构：

[1] Natl Chung Hsing Univ, Inst Networking & Multimedia, Dept Comp Sci & Engn, 250 Kuo Kuang Rd, Taichung 402, Taiwan

[2] Natl Chung Hsing Univ, Dept Comp Sci & Engn, Taichung 402, Taiwan

来源：

2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8 | 2008年

关键词：

D O I：

10.1109/CEC.2008.4631210

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the multiple trajectory search (NITS) is presented for large scale global optimization. The NITS uses multiple agents to search the solution space concurrently. Each agent does an iterated local search using one of three candidate local search methods. By choosing a local search method that best fits the landscape of a solution's neighborhood, an agent may rind its way to a local optimum or the global optimum. We applied the NITS to the seven benchmark problems designed for the CEC 2008 Special Session and Competition on Large Scale Global Optimization.

引用

页码：3052 / +

页数：4

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