Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem
This work addresses a multi-component optimization benchmark for the evolutionary computation community, representing an incremental improvement.
The researchers tackled the bi-objective travelling thief problem by proposing a hybrid evolutionary approach that combines dynamic programming with evolutionary algorithms, achieving results that outperform state-of-the-art methods for the single-objective case.
This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms. We apply it to a new bi-criteria formulation of the travelling thief problem, which is known to the Evolutionary Computation community as a benchmark multi-component optimisation problem that interconnects two classical NP-hard problems: the travelling salesman problem and the 0-1 knapsack problem. Our approach employs the exact dynamic programming algorithm for the underlying Packing-While-Travelling (PWT) problem as a subroutine within a bi-objective evolutionary algorithm. This design takes advantage of the data extracted from Pareto fronts generated by the dynamic program to achieve better solutions. Furthermore, we develop a number of novel indicators and selection mechanisms to strengthen synergy of the two algorithmic components of our approach. The results of computational experiments show that the approach is capable to outperform the state-of-the-art results for the single-objective case of the problem.