Memory-based Multi-Population Genetic Learning for Dynamic Shortest Path Problems

This paper proposes a general algorithm framework for solving dynamic sequence optimization problems (DSOPs). The framework adapts a novel genetic learning (GL) algorithm to dynamic environments via a clustering-based multi-population strategy with a memory scheme, namely, multi-population GL (MPGL). The framework is instantiated for a 3D dynamic shortest path problem, which is developed in this paper. Experimental comparison studies show that MPGL is able to quickly adapt to new environments and it outperforms several ant colony optimization variants.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Y. Diao, C. Li, S. Zeng, M. Mavrovouniotis, S. Yang. Memory-based multi-population genetic learning for dynamic shortest path problems. 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, pp. 2277-2284, 2019. doi:10.1109/CEC.2019.879021