An Efficient Evolutionary Metaheuristic for the Traveling Repairman (Minimum Latency) Problem

Abstract

In this paper we revisit the memetic evolutionary family of metaheuristics, called Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), whose members combine Furuhashi's Bacterial Evolutionary Algorithm and various discrete local search techniques. These algorithms have proven to be efficient approaches for the solution of NP-hard discrete optimization problems such as the Traveling Salesman Problem (TSP) with Time Windows. This paper presents our results in solving the Traveling Repairman Problem (also called Minimum Latency Problem) with a DBMEA variant. The results are compared with state-of-the-art heuristics found in the literature. The DBMEA in most cases turned out to be faster than all other methods, and for the bigger benchmark instances it was also found to have better solutions than the former best-known results. Based on these test results we claim to have found the best approach and thus we suggest the use of the DBMEA for the Traveling Repairman Problem, especially for large instances.

Keywords

• Metaheuristics
• Traveling repairman problem
• Minimum latency problem
• Discrete optimization
• Memetic algorithm