Solving the Travelling Thief Problem based on Item Selection Weight and Reverse Order Allocation
This work provides an incremental improvement for researchers and practitioners working on combinatorial optimization problems, specifically the Travelling Thief Problem.
This paper addresses the Travelling Thief Problem (TTP), a complex optimization problem combining the Travelling Salesman Problem and the 0-1 Knapsack Problem. The authors propose an algorithm that uses a novel item selection weight and reverse order allocation, demonstrating its efficiency in outperforming or matching current state-of-the-art heuristic solutions on benchmark TTP instances.
The Travelling Thief Problem (TTP) is a challenging combinatorial optimization problem that attracts many scholars. The TTP interconnects two well-known NP-hard problems: the Travelling Salesman Problem (TSP) and the 0-1 Knapsack Problem (KP). Increasingly algorithms have been proposed for solving this novel problem that combines two interdependent sub-problems. In this paper, TTP is investigated theoretically and empirically. An algorithm based on the score value calculated by our proposed formulation in picking items and sorting items in the reverse order in the light of the scoring value is proposed to solve the problem. Different approaches for solving the TTP are compared and analyzed; the experimental investigations suggest that our proposed approach is very efficient in meeting or beating current state-of-the-art heuristic solutions on a comprehensive set of benchmark TTP instances.