Learning-Inspired Immune Algorithm for Multiobjective-Optimized Multirobot Maritime Patrolling

Li Huang, Meng Chu Zhou, Hua Han, Shouguang Wang, Aiiad Albeshri

Research output: Contribution to journalArticlepeer-review


Multirobot patrolling systems with various sensing and communications devices are deployed to guarantee maritime safety. Patrolling path planning for multiple robots can be modeled as a multiobjective optimization problem. The positions of patrolling nodes impact the length of patrolling paths and execution efficiency of robots. To compute them, a huge solution space is encountered. Besides, multiple patrolling nodes on the same line lead to the same patrolling scheme. Thus, how to promote solution (population) diversity becomes a new challenge. To tackle it, this work proposes a learning-inspired immune algorithm. It uses the historical information in the previous generations during iterations to realize a learning process. Unlike saving all the individuals themselves and training a model for them, the useful historical information is extracted by using upper confidence bound-based and actor-critic-inspired methods. Both time consumption and storage space can be dramatically saved. The experimental results indicate that the proposed algorithm can generate multiple patrolling schemes for the decision makers and outperforms the state-of-the-art.

Original languageEnglish (US)
Pages (from-to)9870-9881
Number of pages12
JournalIEEE Internet of Things Journal
Issue number6
StatePublished - Mar 15 2024

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications


  • Actorâ€Â"critic
  • immune algorithm
  • multiobjective optimization
  • multirobot maritime patrolling
  • upper confidence bound (UCB)


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