An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field-Reference-Cited by-同舟云学术

An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field

Published:2018-07 Issue: Volume:453 Page:227-238
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Bai Xiaoshan,Yan Weisheng,Ge Shuzhi Sam,Cao Ming

Funder

National Natural Science Foundation of China

China Scholarship Council

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference37 articles.

1. A genetic algorithm for shortest path routing problem and the sizing of populations;Ahn;IEEE Trans. Evolut. Comput.,2002

2. Parallel Metaheuristics: A New Class of Algorithms;Alba,2005

3. Optimal partitioning for spatiotemporal coverage in a drift field;Bakolas;Automatica,2013

4. Decentralized task allocation for surveillance systems with critical tasks;Binetti;Robot. Auton. Syst.,2013

5. Autonomous underwater vehicles for scientific and naval operations;Bovio;Annu. Rev. Control,2006

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