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Multi-strategy improved salp swarm algorithm and its application in reliability optimization.

Dongning Chen1,2, Jianchang Liu1,2, Chengyu Yao3

  • 1Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China.

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|April 18, 2022
PubMed
Summary
This summary is machine-generated.

A new hybrid Salp Swarm Algorithm (SSA) improves optimization speed and accuracy. This enhanced algorithm, DCORSSA-PSO, shows superior performance in benchmark tests and system reliability design.

Keywords:
T-S fault treecentroid opposition-based learningsalp swarm algorithmsocial learningsystem reliability optimization

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Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Swarm Intelligence

Background:

  • Standard Salp Swarm Algorithm (SSA) faces challenges in convergence speed and solution precision.
  • Existing optimization algorithms may struggle with local optima and inter-dimensional interference.

Purpose of the Study:

  • To propose a novel hybrid Salp Swarm Algorithm (SSA) named DCORSSA-PSO.
  • To enhance the convergence speed, solution precision, and robustness of the SSA.
  • To improve system reliability optimization design.

Main Methods:

  • Incorporated a dimension-by-dimension centroid opposition-based learning strategy into SSA.
  • Introduced a random factor in the followers' position update equation.
  • Integrated the social learning strategy from Particle Swarm Optimization (PSO).

Main Results:

  • DCORSSA-PSO demonstrated significantly improved solution precision and convergence speed on benchmark functions compared to SSA and other algorithms.
  • Statistical tests (Wilcoxon, Friedman) confirmed the enhanced robustness of DCORSSA-PSO.
  • Application to T-S fault tree system reliability design yielded lower failure probabilities under cost constraints.

Conclusions:

  • The proposed DCORSSA-PSO effectively overcomes limitations of the standard SSA.
  • The hybrid approach enhances search randomness and the ability to escape local optima.
  • DCORSSA-PSO offers a superior method for system reliability optimization design.