Memetic Algorithm with Adaptive Local Search Depth for Large Scale Global Optimization

被引：0

作者：

Liu, Can ^{[1
]}

Li, Bin ^{[2
]}

机构：

[1] Univ Sci & Technol China, Nat Inspired Computat & Applicat Lab, Hefei 230026, Peoples R China

[2] Univ Sci & Technol China, CAS Key Lab, Hefei 230026, Peoples R China

来源：

2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2014年

关键词：

Memetic algorithms; Local Search Depth; Differential Search Algorithm; Solis and Wets' Algorithm;

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：

Memetic algorithms (MAs) have been recognized as an effective algorithm framework for solving optimization problems. However, the exiting work mainly focused on the improvement for search operators. Local Search Depth (LSD) is a crucial parameter in MAs, which controls the computing resources assigned for local search. In this paper, an Adaptive Local Search Depth (ALSD) strategy is proposed to arrange the computing resources for local search according to its performance dynamically. A Memetic Algorithm with ALSD (MA-ALSD) is presented, its performance and the effectiveness of ALSD are testified via experiments on the LSGO test suite issued in CEC'2012.

引用

页码：82 / 88

页数：7

共 31 条

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