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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 4/2021

06-10-2020 | Original Article

A hybrid many-objective competitive swarm optimization algorithm for large-scale multirobot task allocation problem

Authors: Fei Xue, Tingting Dong, Siqing You, Yan Liu, Hengliang Tang, Lei Chen, Xi Yang, Juntao Li

Published in: International Journal of Machine Learning and Cybernetics | Issue 4/2021

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Abstract

Large-scale multi-robot task allocation (MRTA) problem is an important part of intelligent logistics scheduling. And the load capacity of robot and picking station are important factors affecting the MRTA problem. In this paper, the MRTA problem is built as a many-objective optimization model with four objectives, which takes the load capacity of single robot, single picking station, all robots and all picking stations into account. To solve the model, a hybrid many-objective competitive swarm optimization (HMaCSO) algorithm is designed. The novel selection method employing two different measurement mechanisms will form the mating selection operation. Then the population will be updated by employing the competitive swarm optimization strategy. Meanwhile, the environment selection will play a role in choosing the excellent solution. To prove the superiority of our approach, there are two series of experiments are carried out. On the one hand, our approach is compared with other five famous many-objective algorithms on benchmark problem. On the other hand, the involved algorithms are applied in solving large-scale MRTA problem. Simulation results prove that the performance of our approach is superior than other algorithms.
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Metadata
Title
A hybrid many-objective competitive swarm optimization algorithm for large-scale multirobot task allocation problem
Authors
Fei Xue
Tingting Dong
Siqing You
Yan Liu
Hengliang Tang
Lei Chen
Xi Yang
Juntao Li
Publication date
06-10-2020
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 4/2021
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01213-4

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