A survey on parallel ant colony optimization

被引:176
作者
Pedemonte, Martin
Nesmachnow, Sergio
Cancela, Hector
机构
[1] Facultad de Ingeniería, Universidad de la República
关键词
Ant colony optimization; Parallel implementations; Taxonomy; ALGORITHM; SYSTEM; SEARCH; ACO; METAHEURISTICS; IMPLEMENTATION;
D O I
10.1016/j.asoc.2011.05.042
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Ant colony optimization (ACO) is a well-known swarm intelligence method, inspired in the social behavior of ant colonies for solving optimization problems. When facing large and complex problem instances, parallel computing techniques are usually applied to improve the efficiency, allowing ACO algorithms to achieve high quality results in reasonable execution times, even when tackling hard-to-solve optimization problems. This work introduces a new taxonomy for classifying software-based parallel ACO algorithms and also presents a systematic and comprehensive survey of the current state-of-the-art on parallel ACO implementations. Each parallel model reviewed is categorized in the new taxonomy proposed, and an insight on trends and perspectives in the field of parallel ACO implementations is provided. (C) 2011 Elsevier B. V. All rights reserved.
引用
收藏
页码:5181 / 5197
页数:17
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