An Ant Colony Optimisation Inspired Crossover Operator for Permutation Type Problems

Abstract

Meta-heuristic methods are commonly applied to difficult permutation type problems such as the Traveling Salesman Problem (TSP). Genetic Algorithms (GA) and Ant Colony Optimisation (ACO) are two of the most successful methods. However, a GA requires specialist crossover operators for permutation problems to avoid repetition. This paper presents a novel crossover operator, ACOX, inspired by ACO which can essentially combine both meta-heuristic methods. When applied to a range of TSP instances the ACOX crossover method demonstrates considerable improvements over standard GA crossover operators. ACOX is able to achieve solutions within 4% of the optimal for TSP instances of several thousand cities without using any local search methodologies.