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.
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 language | English |
|---|---|
| Pages (from-to) | 52-63 |
| Number of pages | 12 |
| Journal | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 13 Jan 2020 |