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INNA: An improved neural network algorithm for solving reliability optimization problems.

Tanmay Kundu1, Harish Garg2

  • 1Department of Mathematics, Chandigarh University, Mohali, Punjab 140413 India.

Neural Computing & Applications
|August 8, 2022
PubMed
Summary
This summary is machine-generated.

An improved neural network algorithm (INNA) enhances the reliability-redundancy allocation problem (RRAP) by balancing exploration and exploitation. This novel approach offers superior performance compared to existing meta-heuristics for complex optimization tasks.

Keywords:
Constrained optimizationNeural network algorithmReliability redundancy allocation problemTeaching–learning-based optimization

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Area of Science:

  • Engineering
  • Computer Science
  • Operations Research

Background:

  • The reliability-redundancy allocation problem (RRAP) is crucial for system design.
  • Existing neural network algorithms (NNA) show strong global search but suffer from poor exploitation and slow convergence.
  • Addressing these limitations is key for practical optimization applications.

Purpose of the Study:

  • To introduce an improved neural network algorithm (INNA) for RRAP with nonlinear resource constraints.
  • To simultaneously consider component reliability and redundancy allocation.
  • To enhance the balance between exploration and exploitation in optimization algorithms.

Main Methods:

  • Reconstruction of the NNA search procedure.
  • Implementation of a novel logarithmic spiral search operator.
  • Integration of the learner phase strategy from teaching-learning-based optimization (TLBO).

Main Results:

  • INNA demonstrated superior performance on seven benchmark reliability optimization problems.
  • Statistical analysis using Wilcoxon sign-rank and Multiple comparison tests confirmed the significance of INNA's results.
  • The proposed INNA algorithm outperformed existing meta-heuristics in the literature.

Conclusions:

  • INNA provides a significant advancement in solving the RRAP.
  • The algorithm achieves a better balance between exploration and exploitation, leading to improved efficiency.
  • INNA is a highly competitive and effective tool for complex reliability optimization problems.