Solving dynamic TSP with evolutionary approach in real time

Many real world optimization problems are time-dependent and some of them can be modeled by the dynamic TSPs (DTSPs). A DTSP is harder than a general TSP, which is a NP-hard problem, because the city number and the cost matrix of a DTSP are time varying. Although DTSP is a very common and important model in real world systems, few literatures have discussed this related issues. There are many open questions about DTSP urgently needed to be answered. We first give a mathematical model and the optimization objective for DTSP. Then we discuss why evolutionary algorithms (EAs) are effective for solving DTSPs and give some key points for designing efficient DTSP EAs. By defining three dynamic operators, we proposed an evolutionary algorithm for DTSPs. The experiments show the new algorithm is suitable for solving DTSPs. At the end, we also give some preliminary ideas for reinforcing the efficiency of EAs for DTSPs.