Ant colony optimization-evolutionary hybrid optimization with translation of problem representation

Different hybrid optimization metaheuristics (see the works of Talbi for classification) either assume the embedding of one algorithm (usually a metaheuristic) in another (for instance, a local search inside an evolutionary algorithm-a memetic algorithm) or creating a chain of algorithms. In this paper, such a chain combination of two algorithms (namely, the Ant Colony Optimization and Evolutionary Algorithm) is presented. However, because of the intrinsic differences between the two algorithms (a vector of labels and a pheromone table when solving the traveling salesman problem, for example), several dedicated algorithms for translating the solutions between these two representations of the problem are proposed. The hybrid algorithm constructed with the application of the translation methods turns out to be significantly better in solving the TSP compared to non-hybrid versions (relevant experimental results are presented and discussed). This paves the way for new possibilities of constructing hybrid metaheuristics by putting together completely different ones (using different representations); the impact of the presented research is aimed far beyond the hybridization of only ant colony optimization and evolutionary algorithm.