Hybrid Ant Colony Optimization Using Memetic Algorithm for Traveling Salesman Problem

Abstract

Ant colony optimization was originally presented under the inspiration during collective behavior study results on real ant system, and it has strong robustness and easy to combine with other methods in optimization. Although ant colony optimization for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little research is conducted on the optimum configuration strategy for the adjustable parameters in the ant colony optimization, and the performance of ant colony optimization depends on the appropriate setting of parameters which requires both human experience and luck to some extend. Memetic algorithm is a population-based heuristic search approach which can be used to solve combinatorial optimization problem based on cultural evolution. Based on the introduction of these two meta-heuristic algorithms, a novel kind of adjustable parameters configuration strategy based on memetic algorithm is developed in this paper, and the feasibility and effectiveness of this approach are also verified through the famous traveling salesman problem (TSP). This hybrid approach is also valid for other types of combinational optimization problems