Bounds for the frequency assignment problem
Discrete Mathematics
Lower bounding techniques for frequency assignment
Discrete Mathematics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Population-based and learning-based metaheuristic algorithms for the graph coloring problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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This paper introduces a multiagent optimization algorithm inspired by the collective behavior of social insects. In this method, each agent encodes a possible solution of the problem to solve, and evolves in a way similar to real life insects. We test the algorithm on a classical difficult problem, the k-coloring of a graph, and we compare its performance in relation to a standard genetic algorithm and another multiagent system. The results show that this algorithm is faster and outperforms the other methods for a range of random graphs with different orders and densities. Moreover, the method is easy to adapt to solve different NP-complete problems.