Dynamic programming operators for the bi-objective Traveling Thief Problem

The traveling thief problem (TTP) has emerged as a realistic multi-component problem that poses a number of challenges to traditional optimizers. In this paper we propose different ways to incorporate dynamic programming (DP) as a local optimization operator of population-based approaches to the biobjective TTP. The DP operators use different characterizations of the TTP instance to search for packing plans that improve the best current solutions. We evaluate the efficiency of the DP-based operators using TTP instances of up to 33810 cities and 338100 items, and compare the results of the DP operators with state-of-the-art algorithms for these instances. Our results show that DP-based approaches, applied individually and in combination with other types of operators, can produce good approximations of the Pareto sets for these problems.