A novel Physarum-Based ant colony system for solving the real-world traveling salesman problem

Yuxiao Lu, Yuxin Liu, Chao Gao, Li Tao, Zili Zhang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The solutions to Traveling Salesman Problem can be widely applied in many real-world problems. Ant colony optimization algorithms can provide an approximate solution to a Traveling Salesman Problem. However, most ant colony optimization algorithms suffer premature convergence and low convergence rate. With these observations in mind, a novel ant colony system is proposed, which employs the unique feature of critical tubes reserved in the Physaurm-inspired mathematical model. A series of experiments are conducted, which are consolidated by two realworld Traveling Salesman Problems. The experimental results show that the proposed new ant colony system outperforms classical ant colony system, genetic algorithm, and particle swarm optimization algorithm in efficiency and robustness.

Original languageEnglish
Pages (from-to)173-180
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8794
DOIs
StatePublished - 2014
Externally publishedYes

Keywords

  • Ant Colony System
  • Meta-Heuristic Algorithm
  • Physarum-InspiredMathematical Model
  • Real-World Traveling Salesman Problem

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