A Decomposition and Dominance-Based Multiobjective Artificial Bee Colony Algorithm for Multiple Sequence Alignment
release_2pmklqflsfe37gld3f4h6g3lta
by
Lei Ye
Abstract
The multiple sequence alignment (MSA) problem is essential in biological research for finding a specific relationship between the biologic sequences and their function. This study proposes a decomposition and dominance-based multiobjective artificial bee colony optimization algorithm for MSA (MOABC/D-MSA). MOABC/D-MSA uses three kinds of searching strategies to obtain a group of nondominated solutions with high quality and diversity of an MSA problem. A decomposition-based employed bee strategy is proposed to search for high-performance solutions of the MSA, while insuring their diversity. A nondominated sorting-based onlooker strategy searches for the solutions near the Pareto front (PF) to guide the subsequent searching. The scout bee strategy facilitates the algorithm to get out of the local optimal. A comparative experiment is implemented on BAliBASE 3.0, a benchmark for MSA algorithms. Experimental results show that the proposed algorithm has competitive performance with state-of-the-art metaheuristic algorithms. Furthermore, nondominated solutions of MOABC/D-MSA have a more uniform distribution in the objective space.
In application/xml+jats
format
Archived Files and Locations
application/pdf 2.1 MB
file_mqxjmsms4zd3xg5rwrozq45miu
|
downloads.hindawi.com (publisher) web.archive.org (webarchive) |
access all versions, variants, and formats of this works (eg, pre-prints)
Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar