Efficiently solving the thief orienteering problem with a max-min ant colony optimization approach

TL;DR

This paper introduces a novel max-min ant colony optimization method combined with a heuristic to efficiently solve the Thief Orienteering Problem, outperforming existing methods on extensive benchmarks.

Contribution

It presents a new swarm-intelligence based approach with a randomized packing heuristic for ThOP, achieving superior results over prior solutions.

Findings

Outperforms existing methods on 432 benchmark instances

Significant improvements in solution quality

Effective combination of ant colony optimization with heuristics

Abstract

We tackle the Thief Orienteering Problem (ThOP), an academic multi-component problem that combines two classical combinatorial problems, namely the Knapsack Problem and the Orienteering Problem. In the ThOP, a thief has a time limit to steal items that distributed in a given set of cities. While traveling, the thief collects items by storing them in their knapsack, which in turn reduces the travel speed. The thief has as the objective to maximize the total profit of the stolen items. In this article, we present an approach that combines swarm-intelligence with a randomized packing heuristic. Our solution approach outperforms existing works on almost all the 432 benchmarking instances, with significant improvements.