NDLI: Population-based and learning-based metaheuristic algorithms for the graph coloring problem

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Population-based and learning-based metaheuristic algorithms for the graph coloring problem

Content Provider	ACM Digital Library
Author	Chalupa, David
Abstract	In this paper, two new metaheuristic algorithms for the graph coloring problem are introduced. The first one is a population-based multiagent evolutionary algorithm (MEA), using a multiagent system, where an agent represents a tabu search procedure. Rather than using a single long-term local search procedure, it uses more agents representing short term local search procedures. Instead of a specific crossover, MEA uses relatively general mechanisms from artificial life, such as lifespans and elite list [3, 4]. We are introducing and investigating a new parametrization system, along with a mechanism of reward and punishment for agents according to change in their fitness. The second algorithm is a pseudo-reactive tabu search (PRTS), introducing a new online learning strategy to balance its own parameter settings. Basically, it is inspired by the idea to learn tabu tenure parameters instead of using constants. Both algorithms empirically outperform basic tabu search algorithm TabuCol [8] on the well-established DIMACS instances [10]. However, they achieve this by using different strategies. This indeed shows a difference in potential of population-based and learning-based graph coloring metaheuristics.
Starting Page	465
Ending Page	472
Page Count	8
File Format	PDF
ISBN	9781450305570
DOI	10.1145/2001576.2001641
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-07-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Multiagent systems Graph coloring Tabu search Metaheuristics Pseudo-reactive tabu search Parameter learning
Content Type	Text
Resource Type	Article

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