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A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems.

Tanmay Kundu1, Deepmala1, Pramod K Jain2

  • 1PDPM IIITDM: PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur, MP India.

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Summary
This summary is machine-generated.

A new hybrid optimization algorithm, hybrid salp swarm algorithm with teaching-learning based optimization (HSSATLBO), enhances system reliability. This novel approach effectively solves complex reliability redundancy allocation problems (RRAP) with nonlinear constraints.

Keywords:
Constrained optimizationReliability redundancy allocation problemSalp swarm algorithmTLBO

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Area of Science:

  • Optimization Algorithms
  • Reliability Engineering
  • Meta-heuristic Computing

Background:

  • Salp Swarm Algorithm (SSA) is a recent meta-heuristic algorithm effective for complex problems but suffers from slow convergence.
  • Poor exploitation ability in SSA necessitates improvements for better exploration-exploitation balance.

Purpose of the Study:

  • To propose a novel hybrid optimization algorithm, HSSATLBO, for solving reliability redundancy allocation problems (RRAP) with nonlinear resource constraints.
  • To enhance system reliability by optimizing redundancy and component reliability choices.

Main Methods:

  • Developed a hybrid Salp Swarm Algorithm with Teaching-Learning Based Optimization (HSSATLBO).
  • Integrated SSA's global search with TLBO's fast convergence for improved optimization.
  • Tested HSSATLBO on seven diverse reliability optimization benchmark problems, including series, complex, and large-scale systems.

Main Results:

  • HSSATLBO demonstrated superior performance in maximizing system reliability across various benchmark problems.
  • Comparative analysis showed HSSATLBO outperforming existing meta-heuristic algorithms and SSA variants.
  • Statistical tests (Wilcoxon sign-rank, multiple comparison) confirmed the significance of HSSATLBO's results.

Conclusions:

  • HSSATLBO offers a significant improvement over existing methods for RRAP.
  • The hybrid algorithm provides a promising and effective tool for reliability optimization.
  • HSSATLBO achieves a better balance between exploration and exploitation for enhanced performance.