Agent-based Self-organized Constructive Heuristics for Travelling Salesman Problem

Abstract

Travelling salesman problem (TSP) is a classic combinatorial optimization problem and has become a touchstone of many optimization algorithms. Constructive heuristics with the features of low complexity and using problem knowledge are widely used in online decision-making and can provide high-quality initial solution for iteration algorithms. In this paper, from an agent-based self-organization perspective, the constructive processes from nodes to Hamiltonian graph of a feasible solution are studied. Based on different constructive processes, three novel agent-based constructive heuristic methods (ACHMs) are proposed, including multi-nodes-based ACHM, loop-based ACHM and multi-loop-based ACHM. These constructive heuristic methods build different agent models based on node and loop respectively, and set varied agent actions to make global feasible solutions emerge gradually. Finally, compared with nearest neighbor algorithm and self-organizing mapping, the better performances of these algorithms for TSP are verified by the computational experiments.