Solving the Travelling Thief Problem based on Item Selection Weight and Reverse Order Allocation

TL;DR

This paper introduces a new algorithm for the Travelling Thief Problem that uses item selection weight and reverse order allocation, demonstrating superior performance over existing heuristics through extensive experiments.

Contribution

The paper proposes a novel scoring-based item selection and reverse sorting algorithm for TTP, improving solution quality and efficiency.

Findings

The proposed method outperforms current state-of-the-art heuristics on benchmark instances.

Experimental results show high efficiency and effectiveness of the new approach.

Theoretical and empirical analysis validate the algorithm's superiority.

Abstract

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.