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NERS_HEAD: a new hybrid evolutionary algorithm for solving graph coloring problem
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DOI：10.1007/s00500-023-08413-4.png)
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Abstract
En 中文
The graph coloring problem is an NP-hard problem. Currently, one of the most effective methods to solve this problem is a hybrid evolutionary algorithm. This paper proposes a hybrid evolutionary algorithm NERS_HEAD with a new elite replacement strategy. In NERS_HEAD, a method to detect the local optimal state is proposed so that the evolutionary process can jump out of the local optimal state by introducing diversity on time; a new elite structure and a replacement strategy are designed to increase the diversity of the evolutionary population so that the evolution process can not only converge quickly but also jump out of the local optimal state in time. The comparison experiments with current excellent graph coloring algorithms on 59 DIMACS benchmark instances show that NERS_HEAD can effectively improve the efficiency and success rate of solving graph coloring problems.
Keywords:
Graph coloring problem
Hybrid evolutionary algorithm
Tabu search
Combinatorial optimization