Ant Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem

Michalis Mavrovouniotis, Shengxiang Yang, Mien Van, Changhe Li, Marios Polycarpou

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
160 Downloads (Pure)

Abstract

Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This paper investigates existing ant colony optimization algorithms specifically designed
for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic travelling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are
the most important when designing ant colony optimization algorithms in dynamic environments.
Original languageEnglish
Pages (from-to)52-63
Number of pages12
JournalIEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
Volume15
Issue number1
DOIs
Publication statusPublished - 13 Jan 2020

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