Title: CS-ABC: a cooperative system based on artificial bee colony to resolve the DNA fragment assembly problem

Authors: Elamine Zemali; Abdelmadjid Boukra

Addresses: LSI Laboratory, Department of Computer Science, University of Science and Technologies, Houari Boumediene (USTHB), Algiers, Algeria ' LSI Laboratory, Department of Computer Science, University of Science and Technologies, Houari Boumediene (USTHB), Algiers, Algeria

Abstract: DNA Fragment Assembly (DFA) problem is one of the most active research areas in bioinformatics. It consists in assembling a set of DNA fragments to determine the complete genome sequence. Because of the large number of fragments to assemble, this problem is classified as a NP-hard optimisation problem. Thus, in order to deal with the large search space of such problem, we propose a new cooperative approach involving a set of metaheuristics. The proposed cooperative approach, named CS-ABC, is based on artificial bee colony algorithm. In this approach, metaheuristics cooperate as bees with artificial bee colony algorithm to improve the exploration and exploitation ability, forming a cooperative system. The use of a set of metaheuristics improves naturally the exploration ability since each one of them explores differently the search space. The communication between these metaheuristics is established through a shared memory. The exploitation is also enhanced by using different efficient DFA methods communicating according to the master-slave model. In the computational experiment we firstly, analyse the proposed method behaviour resolving DFA problem. Then, we compare its performance against numerous DFA methods with noiseless and noisy data based on three models of error. The proposed method has obtained promising and encouraging results.

Keywords: DNA fragment assembly problem; cooperation; metaheuristics; bioinformatics; ant colony system; biogeography based optimisation; artificial bee colony algorithm.

DOI: 10.1504/IJDMB.2018.10017727

International Journal of Data Mining and Bioinformatics, 2018 Vol.21 No.2, pp.145 - 168

Accepted: 02 Oct 2018
Published online: 27 Nov 2018 *

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