2 research outputs found
A Distribution Evolutionary Algorithm for Graph Coloring
Graph Coloring Problem (GCP) is a classic combinatorial optimization problem
that has a wide application in theoretical research and engineering. To address
complicated GCPs efficiently, a distribution evolutionary algorithm based on
population of probability models (DEA-PPM) is proposed. Based on a novel
representation of probability model, DEA-PPM employs a Gaussian orthogonal
search strategy to explore the probability space, by which global exploration
can be realized using a small population. With assistance of local exploitation
on a small solution population, DEA-PPM strikes a good balance between
exploration and exploitation. Numerical results demonstrate that DEA-PPM
performs well on selected complicated GCPs, which contributes to its
competitiveness to the state-of-the-art metaheuristics
Solving Graph Coloring Problems Using Discrete Artificial Bee Colony
ç‘æ³¢å¤§å¦ (University of Tsukuba)201