Research Article
A Multi-objective Artificial Bee Colony Algorithm for Multiple Sequence Alignment
@INPROCEEDINGS{10.1007/978-3-030-97124-3_44, author={Ying Yu and Chen Zhang and Lei Ye and Ming Yang and Changsheng Zhang}, title={A Multi-objective Artificial Bee Colony Algorithm for Multiple Sequence Alignment}, proceedings={Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings}, proceedings_a={SIMUTOOLS}, year={2022}, month={3}, keywords={Multiple sequence alignment Multi-objective optimization Artificial bee colony optimization}, doi={10.1007/978-3-030-97124-3_44} }- Ying Yu
Chen Zhang
Lei Ye
Ming Yang
Changsheng Zhang
Year: 2022
A Multi-objective Artificial Bee Colony Algorithm for Multiple Sequence Alignment
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-97124-3_44
Abstract
The multiple sequence alignment (MSA) problem is essential in biological research for finding specific relationship between the biologic sequences and their functions. This paper proposes a multi-objective artificial bee colony optimization algorithm for MSA (MOABC-MSA), which uses three kinds of searching to optimize a multi-objective MSA problem. The employed bee searching aims to make the solutions converge to the Pareto front (PF) of the problem; the onlooker bee accelerates the convergence speed; the scout bee facilitates the algorithm to avoid the local optimal. A comparative experiment is implemented on BAliBASE 3.0, a MSA benchmark. Experimental results show that the proposed algorithm has competitive performance with state-of-the-art metaheuristic algorithms.
