Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30833
Title: Memory-based multi-population genetic learning for dynamic shortest path problems
Authors: DIao, Yiya 
Li, Changhe 
Zeng, Sanyou 
Mavrovouniotis, Michalis 
Yang, Shengxiang 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: ant colony optimization;clusteringbased multi-population;dynamic sequence optimization;dynamic shortest path;genetic learning
Issue Date: 10-Jun-2019
Source: 2019 IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 10 - 13 June 2019
Conference: 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings 
Abstract: 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.
URI: https://hdl.handle.net/20.500.14279/30833
ISBN: 9781728121536
DOI: 10.1109/CEC.2019.8790211
Rights: © IEEE
Type: Conference Papers
Affiliation : China University of Geosciences 
Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems 
China University of Geosciences 
University of Cyprus 
De Montfort University 
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