Parallel multi-objective optimization approaches for protein encoding

Gonzalez-Sanchez, Belen; Vega‐Rodríguez, Miguel A.; Santander-Jiménez, Sergio

doi:10.1007/s11227-021-04073-z

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…There is no single optimal solution in multi‐objective optimization, but a Pareto set, where each objective function is optimized simultaneously 51 . To better explore different directions of the search space, we analyze the MOBOA 52 algorithm design, with a probability Ps. It can be seen that this will improve the switching between local and global searches that is inherited from the BOA proposal.…”

Section: Rbfnn Boa and Its Modified Formmentioning

confidence: 99%

Development of a novel RBFNN‐trained nonlinear channel equalizer based on GDEBOA technique

Mohapatra¹,

Rout²,

Nayak

2023

Adaptive Control & Signal

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The equalization of digital channels is generally known to be a nonlinear classification problem. Applications such as these can benefit from networks that approximate nonlinear mappings. It gets good performance by adjusting only one coefficient and one center closest to the input vector of the radial basis function network (RBFN), which is a simplified version of stochastic gradient techniques. Artificial Neural Networks (ANNs) are suitable for channel equalization because they have the capability to map between variables. Having only one hidden layer in Radial Basis Function Neural Network (RBFNN) makes it the most preferred equalizer to mitigate distortions in the channels. The ability to equalize nonlinear channels is strength of radial RBFNN, which is a simplified version of stochastic gradient techniques. The conventional “hit and trial” approach poses the greatest difficulty when designing RBFNN Equalizers. As a solution to these limitations, this work suggests a training strategy based on Gaussian distribution estimation strategy (GDE) in hybrid butterfly optimization algorithm (GDEBOA) for RBFNN channel equalizer. The proposed RBFNN equalizer is being trained using a population‐based optimization algorithm. An algorithm, called GDEBOA, based on a GDE approach is developed in this paper. The proposed training scheme significantly outperforms existing metaheuristic algorithms in terms of mean square error (MSE) and bit error rate (BER). Furthermore, based on burst error scenarios and bit error probability (BEP), the proposed method has demonstrated greater robustness than other methods when faced with such scenarios. The effectiveness of the suggested scheme over a wide range of signal‐to‐noise ratios has been validated by several simulation studies. Additionally, statistical importance of the suggested technique is analyzed. It is visualized that GDEBOA performs better than existing algorithms for training RBFNN equalizer.

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Section: Rbfnn Boa and Its Modified Formmentioning

confidence: 99%

Development of a novel RBFNN‐trained nonlinear channel equalizer based on GDEBOA technique

Mohapatra¹,

Rout²,

Nayak

2023

Adaptive Control & Signal

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A fast nondominated sorting-based MOEA with convergence and diversity adjusted adaptively

Gao

Zhang

et al. 2023

J Supercomput

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GPU-accelerated non-dominated sorting genetic algorithm III for maximizing protein production

Kim,

Kim

2024

era

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<abstract><p>Maximizing protein expression levels poses a major challenge in bioengineering. To increase protein expression levels, numerous factors, including codon bias, codon context bias, hidden stop codons, homologous recombination, suitable guanine-cytosine ratio, and hairpin loop structure, are crucial and quantified by six objective functions: CAI, CPB, HSC, HD, GC3, and SL. Optimizing these six objectives simultaneously constitutes a multi-objective optimization problem, aiming to identify the favorable Pareto solutions rather than a singular optimal solution. However, achieving satisfactory solutions requires numerous cycles and solutions, thus leading to a large number of functional evaluations. While there are frameworks for multi-objective optimization problems, they often lack efficient support for objective function computation in protein encoding. In this paper, we proposed a method to design a set of coding sequences (CDSs) based on non-dominated sorting genetic algorithm III (NSGA-III), accelerated using NVIDIA graphical processing units (GPUs). Experimental results indicated that our method is 15,454 times faster than the Pymoo framework and is evaluated using 100 solutions and 100 cycles. Since our GPU implementation facilitated the use of larger solutions and more cycles, we were able to design a superior set of CDSs by increasing solutions to 400 and cycles to 12,800. In addition, our NSGA-III-based method consistently surpassed the NSGA-II approach when the number of cycles exceeded 3200 by utilizing 100 solutions. Finally, we observed that a gradual reduction of the mutation probability as the number of cycles increased yielded better quality results than maintaining a fixed mutation probability.</p></abstract>

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Parallel multi-objective optimization approaches for protein encoding

Cited by 3 publications

References 26 publications

Development of a novel RBFNN‐trained nonlinear channel equalizer based on GDEBOA technique

Development of a novel RBFNN‐trained nonlinear channel equalizer based on GDEBOA technique

A fast nondominated sorting-based MOEA with convergence and diversity adjusted adaptively

GPU-accelerated non-dominated sorting genetic algorithm III for maximizing protein production

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