On integrating an Iterated Variable Neighborhood Search within a bi-objective Genetic Algorithm: Sum Coloring of Graphs Case Application

https://doi.org/10.1016/j.endm.2018.03.008Get rights and content

Abstract

The minimum sum coloring of graphs is a variant of the classical graph coloring problem which is known to be NP-hard. The problem consists on minimizing the sum colorings of different graph vertices. In this paper, we propose a new bi-objective model for the underlying problem. We also propose for the resolution a hybrid schema which combines a bi-objective genetic algorithm with an Iterated Variable Neighborhood Search. The proposed approach relies on the use of different dedicated evolutionary operators mainly crossover and mutation. We also note two important features of the Variable Neighborhood Search: the use of destroy/repair method for shaking step and a multi-neighborhood search. Combined methods led us to preliminary promising results.

References (12)

There are more references available in the full text version of this article.

Cited by (2)

  • Local search for weighted sum coloring problem

    2021, Applied Soft Computing
    Citation Excerpt :

    The weighted sum coloring problem (WSCP) is a variant of the sum coloring problem or the weighted vertex coloring problem, so CPLEX provides a possible solution for solving WSCP. Genetic algorithm is a classical metaheuristic algorithm, which is one of the most studied heuristic algorithms to solve graph coloring problems and has been used to solve some graph coloring problems, such as sum coloring problem [25], total graph coloring [26] etc. So, using genetic algorithm to solve WSCP is also a potentially feasible solution.

View full text