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

Abstract

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.