MFEA-IG: A Multi-Task Algorithm for Mobile Agents Path Planning

Mobile agent path planning (MAPP) problem is a typical optimization problem. When we consider multiple agents path planning simultaneously, problems can be seen as multi-task optimization (MTO) problems. The Multi-factorial evolutionary algorithm (MFEA) is one promising technique for MTO problems. W...

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Bibliographic Details
Published in2020 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 7
Main Authors Zhou, Yongjian, Wang, Tonghao, Peng, Xingguang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
Evolutionary computation
Genetic algorithms
knowledge transfer
MFEA
Mobile agents
multi-task optimization
multiple agents path planning
Path planning
Sociology
Statistics
Task analysis
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DOI10.1109/CEC48606.2020.9185906

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Summary:Mobile agent path planning (MAPP) problem is a typical optimization problem. When we consider multiple agents path planning simultaneously, problems can be seen as multi-task optimization (MTO) problems. The Multi-factorial evolutionary algorithm (MFEA) is one promising technique for MTO problems. Within the MFEA some selective individuals that contain useful knowledge are transferred among independent tasks to enhance the convergence. In this work, we investigate what information, except to the selective individuals, should be transferred under the framework of the MFEA. In particular, the difference between the individuals and the estimated optimal solution of the corresponding task is used to calculate {individua}l_{-}{gradient} (IG), which is introduced into the MFEA as additional knowledge for transferring. Empirical studies on nine benchmarks validate the effectiveness of IG based MFEA (MFEA-IG). Moreover, we apply the MFEA-IG to MAPP problems. Simulation results show that the MFEA-IG outperforms the original MFEA and single task EA.
DOI:10.1109/CEC48606.2020.9185906