An ACO algorithm with Lévy Flight for Hard Constraint Satisfaction Problems

Abstract

To solve large-scale and hard constraint satisfaction problems (CSPs), ant colony optimization (ACO) has been studied as one of the metaheuristics. Although ACO has been effective for solving CSPs, it has been sometimes difficult to solve large-scale combinatorial problems because of falling into the local optima. Lévy ACO, an ACO algorithm using Lévy Flight (LF), has been proposed to escape from local optima. Lévy ACO escapces from local optima by interweaving the local search with a global search using LF. However, the fixed parameter (LF parameter) that determines how often LF is used may reduce the efficiency of the search. In this paper, we propose a method to dynamically adjust the LF parameter of Lévy ACO according to the progress of the search. We demonstrate that our method can solve large-scale and hard graph coloring problems, which is one of CSPs, more efficiently than the previous methods.