Abstract:

In recent years, various heuristic optimization methods have been developed. Many of these methods areinspired by swarm behaviors in nature, such as particle swarm optimization (PSO), firefly algorithm (FA)and cuckoo optimization algorithm (COA). Recently introduced COA, has proven its excellent capabilities,such as faster convergence and better global minimum achievement. In this paper a new approach forsolving graph coloring problem based on COA was presented. Since COA at first was presented for solvingcontinuous optimization problems, in this paper we use the COA for the graph coloring problem, we needa discrete COA. Hence, to apply COA to discrete search space, the standard arithmetic operators suchas addition, subtraction and multiplication existent in COA migration operator based on the distance’stheory needs to be redefined in the discrete space. Redefinition of the concept of the difference betweenthe two habitats as the list of differential movements, COA is equipped with a means of solving thediscrete nature of the non-permutation. A set of graph coloring benchmark problems are solved and itsperformance is compared with some well-known heuristic search methods. The obtained results confirmthe high performance of the proposed method.