Transaction of the Japanese Society for Evolutionary Computation
Online ISSN : 2185-7385
ISSN-L : 2185-7385
Original Paper
Solving the Graph Coloring Problem Using Adaptive Artificial Bee Colony
Kui ChenHitoshi Kanoh
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2019 Volume 9 Issue 3 Pages 103-114


Recently, some discrete swarm intelligence algorithms such as particle swarm optimization with hamming distance (HDPSO), similarity artificial bee colony (S-ABC), and discrete firefly algorithm (DFA) have been proposed to solve graph 3-coloring problems (3-GCP) and obtain good results. However, these algorithms use static parameter settings that limit their performance on graphs with various sizes and topology. In this paper, we propose a discrete adaptive artificial bee colony (A-ABC) algorithm that can adjust the parameter automatically during the evolution according to the graph size and the fitness of candidates. For the convenience of comparison, we also propose a fixed ABC (F-ABC), which is identical to A-ABC but using fixed parameter setting during the evolution. A-ABC is simple and high performance. Experiments on 3-GCP show that A-ABC dramatically outperforms its competitors F-ABC, HDPSO, S-ABC, and DFA. We also study the scout bee phase and report that the scout bee phase is not required in solving 3-GCP

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© 2019 The Japanese Society for Evolutionary Computation
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