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Training Feedforward Neural Network Using Enhanced Black Hole Algorithm: A Case Study on COVID-19 Related ACE2 Gene

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Summary

A new Black Hole Algorithm (BHA) and its enhanced version, BHACRW, improve feedforward neural network training. BHACRW demonstrates superior accuracy and performance in classification tasks compared to other algorithms.

Keywords:
Black hole optimization algorithm (BHA)Levy flightMultilayer perceptron (MLP)Neural Network (FNN) training

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Optimization

Background:

  • Traditional and metaheuristic algorithms for training feedforward neural networks (FNNs) often face challenges with slow convergence and local optima.
  • The Black Hole Algorithm (BHA) is introduced as a novel approach to address these limitations in FNN training.

Purpose of the Study:

  • To propose the Black Hole Algorithm (BHA) as an effective training algorithm for FNNs.
  • To enhance BHA by incorporating complementary learning and Levy flight random walk, creating BHACRW, for improved FNN accuracy.
  • To evaluate the performance of BHA and BHACRW in numerical optimization and FNN classification tasks.

Main Methods:

  • The Black Hole Algorithm (BHA) was developed for training feedforward neural networks.
  • The BHACRW algorithm was created by integrating complementary learning components and Levy flight random walk into BHA.
  • Performance was assessed using four benchmark functions for numerical optimization and seven benchmark datasets for FNN classification, including the ACE2 gene expression dataset.

Main Results:

  • BHACRW demonstrated superior performance in numerical optimization compared to standard BHA.
  • BHACRW-trained FNNs achieved higher accuracy and lower mean square error (MSE) than standard BHA and eight other metaheuristic algorithms (WOA, BBO, GSA, GA, CS, MVO, SOS, PSO).
  • The BHACRW-FNN model attained the highest accuracy on the ACE2 gene expression dataset compared to other classifiers.

Conclusions:

  • The proposed BHACRW algorithm offers a robust and effective method for training FNNs, overcoming limitations of traditional and existing metaheuristic approaches.
  • BHACRW shows significant potential for improving classification accuracy in various datasets, including biological data relevant to viral infections.
  • The study validates BHACRW as a promising optimization technique for enhancing machine learning model performance.