Multiobjective Frog-Leaping Optimization for the Study of Ancestral Relationships in Protein Data
该研究提出一种基于蛙跳优化技术的多目标方法，用于从氨基酸序列重建祖先关系，并通过并行计算提升处理效率，在五个真实数据集上验证了方法的有效性。
Among the different scientific domains where metaheuristics find applicability, bioinformatics represents a particularly challenging field due to the multiple complexity factors involved in the processing of biological data. In this context, the exploration of protein sequence data is remarkably increasing the temporal demands of such biological problems, thus motivating the interest in investigating new approaches that effectively combine bioinspired metaheuristics and parallelism. This paper addresses the reconstruction of ancestral relationships from amino acid sequences by using a multiobjective approach based on the shuffled frog-leaping optimization technique. Due to the inherent parallel nature of this approach, we define different parallel schemes aimed at exploiting the computing capabilities of modern cluster platforms. The experiments performed in five real datasets give account of the relevance of using parallelism-aware metaheuristic designs, as well as the need to consider both parallel performance and solution quality when tackling such difficult optimization scenarios.