Population-Based Ant Colony Optimization with New Hierarchical Pheromone Updating Mechanism for DNA Sequence Design Problem

DOI

10.2991/aisr.k.200424.068How to use a DOI?

Keywords

population-based ant colony optimization, DNA sequence design, new pheromone

Abstract

DNA computing research in the last 20 years has attracted the attention of many researchers from various scientific disciplines. Many researchers have succeeded in demonstrating the ability of bio-molecules DNA computing to be used as storage media and biochemical tools as information processing operators. The reaction process between chemical compounds in DNA is probabilistic, which means that one experiment with others can get different results, even in the same conditions. To overcome this problem, the design of the DNA sequence should be received much attention. The DNA sequence design is a process to get a set of DNA sequences that will be unique to each other. The method of DNA sequence formation can be part of an optimization process, in which each DNA sequence must be designed in such a way as not to react with other DNA sequences in the same set. This process can increase the success of a DNA computing experiment. In this paper, an enhanced method form previous research is proposed. Instead of using Ant Colony Optimization (ACO) algorithm, the population-based version of ACO (P-ACO) is used, to take advantage of the population-like optimization algorithm to handle the multi-objective optimization problem. A new hierarchical pheromone updating mechanism is proposed as well to obtain a highly accurate and efficient tool to feed the information for the ants to follows the best path. In this paper, we use the same model for the ants to solve the problem, the Watson-Crick ΔGo37 thermodynamic temperature parameter pairs used as distance among nodes in the model. The result from the proposed method and previous research compared then it shows the proposed method get the relatively better result in terms of the pattern of DNA sequence and the objectives function.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - MISINEM
AU  - ERMATITA
AU  - Dian Palupi RINI
AU  - Reza Firsandaya MALIK
AU  - Tri Basuki KURNIAWAN
PY  - 2020
DA  - 2020/05/06
TI  - Population-Based Ant Colony Optimization with New Hierarchical Pheromone Updating Mechanism for DNA Sequence Design Problem
BT  - Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019)
PB  - Atlantis Press
SP  - 443
EP  - 447
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.200424.068
DO  - 10.2991/aisr.k.200424.068
ID  - 2020
ER  -