Applying ant colony optimization to dynamic binary-encoded problems

Date

2015-04

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Springer

Type

Conference

Peer reviewed

Yes

Abstract

Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is addressed. Usually, permutation-encoded DOPs, e.g., dynamic travelling salesman problems, are addressed using ACO algorithms whereas binary-encoded DOPs, e.g., dynamic knapsack problems, are tackled by evolutionary algorithms (EAs). This is because of the initial developments of the algorithms. In this paper, a binary version of ACO is introduced to address binary-encoded DOPs and compared with existing EAs. The experimental results show that ACO with an appropriate pheromone evaporation rate outperforms EAs in most dynamic test cases.

Description

Keywords

Ant colony optimization, Dynamic optimization problem

Citation

Mavrovouniotis, M. and Yang, S. (2015) Applying ant colony optimization to dynamic binary-encoded problems. EvoApplications 2015: Applications of Evolutionary Computation

Rights

Research Institute

Institute of Artificial Intelligence (IAI)