An artificial bee colony algorithm with a modified choice function for the Traveling Salesman Problem

The Artificial Bee Colony (ABC) algorithm is a swarm intelligence approach which has initially been proposed to solve optimization of mathematical test functions with a unique neighbourhood search mechanism. However, this neighbourhood search mechanism could not be directly applied to combinatorial discrete optimization problems. The employed and onlooker bees need to be equipped with problem-specific perturbative heuristics in order to tackle combinatorial discrete optimization problems. However, there is a large variety of available problem-specific heuristics. In this paper, a hyper-heuristic method, namely a Modified Choice Function (MCF), is applied such that it can regulate the selection of the neighbourhood search heuristics adopted by the employed and onlooker bees automatically. The proposed MCF-based ABC model is implemented using the Hyper-heuristic Flexible Framework (HyFlex). To demonstrate the effectiveness of the proposed model, ten Traveling Salesman Problem (TSP) instances available in HyFlex have been evaluated. The empirical results show that the proposed model is able to statistically outperform four out of five ABC variants throughout the optimization process.