文献类型：期刊文献

英文题名：Ant colony optimization with local search for continuous functions

作者：Qi, Chengming[1]

第一作者：亓呈明

通讯作者：Qi, C.

机构：[1] College of Automation, Beijing Union University, Beijing 100101, China

第一机构：北京联合大学城市轨道交通与物流学院

年份：2011

卷号：204-210

起止页码：1135-1138

外文期刊名：Advanced Materials Research

收录：EI(收录号：20111213782571)

语种：英文

外文关键词：Artificial intelligence - Local search (optimization) - Functions

摘要：Ant algorithms are a recently developed, population-based approach which was inspired by the observation of the behavior of ant colonies. Based on the ant colony optimization idea, we present a hybrid ant colony system (ACS) coupled with a pareto local search (PLS) algorithm, named PACS, and apply to the continuous functions optimization. The ACS makes firstly variable range into grid. In local search, we use the PLS to escape local optimum. Computational results for some benchmark problems demonstrate that the proposed approach has the high search superior solution ability.