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- Title
Artificial Intelligence in Marine Science and Engineering.
- Authors
García Márquez, Fausto Pedro; Papaelias, Mayorkinos; Marini, Simone
- Abstract
A chaotic particle swarm optimization algorithm with speed control (CCPSO) was proposed, which include a chaotic particle strategy, a particle iterative speed control strategy, and a particle mapping space for hybrid scheduling. The results show that, compared with a single scheduling rule, the proposed algorithm improves the average performance of task completion time, task delay time, AGVs travel time and task delay rate by 15.63%, 56.16%, 16.36% and 30.22%, respectively; compared with AGA and RHPA, it reduces the tasks completion time by approximately 3.10% and 2.40%. This Special Issue covers research in Artificial Intelligence in Marine Science and Engineering and shows how to apply it to many different professional areas, e.g., engineering, economics, and management. The simulation results showed that CCPSO can obtain a near-optimal solution in a shorter time and find a better solution when the solution time is sufficient, comparing with the traditional particle swarm optimization algorithm, the adaptive particle swarm optimization algorithm, and the random position particle swarm optimization algorithm.
- Subjects
MARINE engineering; ARTIFICIAL intelligence; MARINE engineers; MARINE sciences; PARTICLE swarm optimization; REINFORCEMENT learning; CONTAINER terminals
- Publication
Journal of Marine Science & Engineering, 2022, Vol 10, Issue 6, p711
- ISSN
2077-1312
- Publication type
Article
- DOI
10.3390/jmse10060711