文章基本信息

标题：Efficient Local Search Limitation Strategies in Memetic Algorithm
本地全文：下载
作者：Yuichi Nagata ; Shigenobu Kobayashi ; Satoshi Tojo 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2010
卷号：25
期号：2
页码：299-310
DOI：10.1527/tjsai.25.299
出版社：The Japanese Society for Artificial Intelligence
摘要：Applications of memetic algorithms (MAs) are usually computationally expensive. In this paper we suggest efficient search limiting strategies for local search used in MAs because local search is the most time consuming part of MAs. The suggested strategies are applied to a recently proposed powerful MA for the capacitated vehicle routing problem (CVRP). Experimental results on the well-known benchmarks show a significant speed-up of 80% in running time without worsening the solution quality. Moreover, the MA dominates state-of-the-art heuristics for the CVRP with respect to both the computation time and the solution quality.
关键词：memetic algorithm ; genetic local search ; vehicle routing ; local search