Repository and Mutation based Particle Swarm Optimization (RMPSO): A new PSO variant applied to reconstruction of Gene Regulatory Network

Autor: Sriyankar Acharyya, Biswajit Jana, Suman K. Mitra
Rok vydání: 2019
Předmět:
Zdroj: Applied Soft Computing. 74:330-355
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2018.09.027
Popis: Particle Swarm Optimization (PSO) is a meta-heuristic approach based on swarm intelligence, which is inspired by the social behaviour of bird flocking or fish schooling. The main disadvantage of the basic PSO is that it suffers from premature convergence. To prevent the process of search from premature convergence as well as to improve the exploration and exploitation capability as a whole, here, in this paper, a modified variant, named Repository and Mutation based PSO (RMPSO) is proposed. In RMPSO variant, apart from applying five-staged successive mutation strategies for improving the swarm best as referred in Enhanced Leader PSO (ELPSO), two extra repositories have been introduced and maintained to store personal best and global best solutions having same fitness values. In each step, the personal and global best solutions are chosen randomly from their respective repositories which enhance exploration capability further, retaining the exploitation capability. The computational experiment on benchmark problem instances shows that in most of the cases, RMPSO performs better than other algorithms in terms of the statistical metrics taken into account. Moreover, the performance of the proposed algorithm remains consistent in most of the cases when the dimension of the problem is scaled up. RMPSO is further applied to a practical scenario: the reconstruction of Gene Regulatory Networks (GRN) based on Recurrent Neural Network (RNN) model. The experimental results ensure that the RMPSO performs better than the state-of-the-art methods in the synthetic gene data set (gold standard) as well as real gene data set.
Databáze: OpenAIRE
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