A Distributed Memetic Algorithm with a semi-greedy operator for the Traveling Salesman Problem

In this work, we propose a distributed Memetic Algorithm for the traveling salesman problem focusing on small and medium-sized instances. The algorithm employs a new crossover operator that favors the fitter of the two parents and develops varying progeny from the same parents to evade premature convergence. In the implementation, we use the High-Level Architecture (HLA) to distribute laborious tasks and lower the lead time of the heuristic. Results show that our proposed approach significantly outperforms other algorithms when tested on instances from the TSPLIB benchmark.