Parallel MOEA Based on Consensus and Membrane Structure for Inferring Phylogenetic Reconstruction - Zhang, Qianqian; Zhang, Jun; Zhong, Yue; Ye, Congming; Min, Xiaoping (IEEE access, 2020)
In recent years, inferring phylogenies has attracted lots of attention in both academic community and various application fields. Phylogenetic inference usually consists of a couple of evolutionary relationships, which can be represented as a phylogenetic tree. The phylogenetic reconstruction problem can be defined as an optimization problem, targeting at finding the most eligible tree among all possible topologies according to a selected criterion. Since the combinatorial number of possible topologies exceeds tolerance, various heuristic and metaheuristic methods have been proposed to find approximate solutions according to the selected criterion. However, different criterions are based on different principle and conflict with each other basically. In this line, scholars has proposed multi-objective evolutionary algorithm (MOEA) based on diverse criteria. Nevertheless, MOEA has suffered unbearable time consumption due to its inherent drawbacks of computational complexity and convergence. By studying the independence between the sub-populations in each time-consuming step of MOEA, the steps without global information can be designed to be executed in parallel, which can fundamentally address computational problems. Effective parallel algorithms designed with the characteristics of modern multicore clusters can solve such problems. In this sense, we propose a parallelized multi-objective evolutionary algorithm (MOEA-MC) by deploying on Spark, which added consensus into evolutionary algorithm to improve the quality of convergence and used membrane structure to keep equal solutions under different weights. In order to assess the performance achieved by the proposal, we have performed comparison among different methods on three real-world datasets separately. The results have certified that the solutions derived from MOEA-MC are superior to traditional methods in all studied datasets. And parallelized MOEA-MC can get dominant position and optimal Pareto-frontier simultaneously within minimal runtime.