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Journal ArticleDOI

Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm

13 Jun 2017-Journal of Bioinformatics and Computational Biology (World Scientific Publishing Company)-Vol. 15, Iss: 4, pp 1750016-1750016

TL;DR: A new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN) is proposed, mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques.

AbstractCorrect inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.

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Citations
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Journal ArticleDOI
TL;DR: RMPSO is applied to a practical scenario: the reconstruction of Gene Regulatory Networks (GRN) based on Recurrent Neural Network (RNN) model and 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 data set.
Abstract: 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.

27 citations

Journal ArticleDOI
TL;DR: Results show the efficiency of MESWSA algorithm for I-V characteristics of solar modules at different operating conditions can serve as a new alternative metaheuristic for parameter estimation of solar cells/PV modules.
Abstract: A highly accurate modeling of photovoltaic (PV) systems from experimental data is a very important task for electronic engineers for efficient design of PV systems. Suitable optimization techniques...

6 citations

Journal ArticleDOI
01 Aug 2019
TL;DR: In this work, three different improved versions of original elephant swarm water search algorithm (ESWSA) is proposed and tested against the present problem of liquid flow control and ESWSA is found to be best efficient algorithm with respect to success rate and computational time.
Abstract: In process industry, liquid flow rate is one of the important variables which need to be controlled to obtain the better quality and reduce the cost of production. The liquid flow rate depends upon number of parameters like sensor output voltage, pipe diameter etc. Conventional approach involves manual tuning of these variables so that optimal flow rate can be achieved which is time consuming and costly. However, estimation of an accurate computational model for liquid flow control process can serve as alternative approach. It is nothing but a non-linear optimization problem. In this work, three different improved versions of original elephant swarm water search algorithm (ESWSA) is proposed and tested against the present problem of liquid flow control. Equations for response surface methodology and analysis of variance are being used as non-linear models and these models are optimized using those newly proposed optimization techniques. The statistical analysis of the obtained results shows that the proposed MESWSA has highest overall efficiency (i.e. 45%) and it outperformed the others techniques for the most of the cases of modeling for liquid flow control process. But one of the major disadvantages of MESWSA is its slow convergence speed. On the other hand, ESWSA is better for finding the best fitness and LESWSA has better stability in output. Moreover, LMESWSA is found to be best efficient algorithm with respect to success rate and computational time. However, all algorithms and models can predict the liquid flow rate with satisfactory accuracy.

3 citations

Journal ArticleDOI
Abstract: Martensitic stainless steels are hard, brittle and notch sensitive; crack formation during welding is frequent. Selection of the levels of welding parameters i.e. the input variables seems to be important and useful in the context of achieving optimum/maximum strength of the welded joint. In the present work, focus is given on identification of the proper combination of input parameters in TIG welding of martensitic stainless steel AISI 420. Welding current, gas flow rate and welding speed have been taken as input parameters. Ultimate tensile strength (UTS) and Ductility or Elongation of the welded joint obtained from tensile test is taken as response parameter. Initially, response surface methodology based face-centered central composite design has been used for mathematical model building and regression analysis. Next, several recently proposed metaheuristics are applied for parametric optimization of TIG welding process to maximize the response parameters. From, the simulated results, a critical operating region for efficient TIG welding is identified in term of maximum UTS and Ductility. Confirmatory tests are also performed to validate our proposed methodology.

3 citations

Journal ArticleDOI
TL;DR: The results show that ESWSA is very efficient process for achieving desired radiation pattern while amplitude only control performed better compare to the others two controlling process for all benchmark problems.
Abstract: Linear antenna array pattern synthesis using computational method is an important task for the electronics engineers and researchers. Suitable optimization techniques are required for solving this kind of problem. In this work, Elephant Swarm Water Search Algorithm (ESWSA) has been used for efficient and accurate designing of linear antenna arrays that generate desired far field radiation pattern by optimizing amplitude, phase and distance of the antenna elements. ESWSA is inspired by water resource search procedure of elephants during drought. Two different fitness functions for two different benchmark problem of linear antenna array have been tested for validation of the proposed methodology. During optimization, three types of synthesis have been used namely: amplitude only, phase only and position only control for all cases antenna array. The results show that ESWSA is very efficient process for achieving desired radiation pattern while amplitude only control performed better compare to the others two controlling process for all benchmark problems.

2 citations


Cites background or methods from "Recurrent neural network-based mode..."

  • ...Elephant Swarm Water Search Algorithm (ESWSA) [33], [34], [35] is a recently proposed metaheuristic which is inspired by water resource search procedure of elephants during drought....

    [...]

  • ...So far, basic ESWSA was applied have been applied on many problems in different disciplines, such as three-bar truss design problem [34], spring design problem [34], reverse engineering of gene regulatory network [33], and modeling of welding process [35]....

    [...]

  • ...[33], [34], [35] initially proposed Basic Elephant Swarm Water Search Algorithm (ESWSA) optimization technique....

    [...]


References
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TL;DR: This review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.
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Book ChapterDOI
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