Ant Population with Dynamic Adjustable Pheromone Accumulation for Constraint Satisfaction Problems

Abstract

To solve large-scale and hard constraint satisfaction problems (CSPs), many meta-heuristics have been studied. Although Ant colony optimization (ACO) has been effective for solving CSPs using pheromone trails in constructing candidate solutions, search efficiency may sometimes reduce depending on how pheromones accumulate. In this paper, we propose an ACO based method which can dynamically adjust several pheromone accumulations. In our method, after dividing into multiple ant populations with different pheromone accumulation, the search, or constructing candidate solutions and pheromone update, is conducted at each population. In addition, the size of each population is adjusted during 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.