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Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO).

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Updated: Nov 10, 2025

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence
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UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight.

Jian Zhang1, Jianan Sheng1, Jiawei Lu1

  • 1School of Mechanical Engineering, Tongji University, Shanghai 200092, China.

Computational Intelligence and Neuroscience
|April 1, 2021
PubMed
Summary

The improved particle swarm optimization (PSO) algorithm, UCPSO, enhances global search by using a cosine inertia weight, uniform initialization, and a rank-based strategy. This boosts performance and avoids local optima.

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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence Algorithms

Background:

  • Particle Swarm Optimization (PSO) is a popular meta-heuristic algorithm known for its efficiency.
  • However, standard PSO can suffer from premature convergence and getting trapped in local optima.
  • Enhancements to PSO parameters, initialization, and topology aim to improve its global search capabilities.

Purpose of the Study:

  • To propose a novel variant of the Particle Swarm Optimization algorithm, termed UCPSO.
  • To enhance the global search ability and overall performance of PSO.
  • To address the limitations of premature convergence and local optima in standard PSO.

Main Methods:

  • Introduced UCPSO, incorporating three key improvements: a cosine inertia weight, uniform particle initialization, and a rank-based strategy.
  • The cosine inertia weight employs a variable-period cosine function for a multi-stage exploration-exploitation balance.
  • Uniform initialization prevents particle aggregation, while the rank-based strategy dynamically adjusts inertia weights to improve swarm exploration and exploitation.

Main Results:

  • Comparative experiments demonstrated the effectiveness of the proposed UCPSO variant.
  • The integrated improvements significantly enhanced the global search ability of the algorithm.
  • UCPSO showed superior performance compared to standard PSO in addressing optimization challenges.

Conclusions:

  • The UCPSO algorithm effectively overcomes the limitations of traditional PSO, particularly premature convergence.
  • The combination of cosine inertia weight, uniform initialization, and rank-based strategy leads to improved optimization performance.
  • UCPSO offers a competitive alternative for complex optimization problems requiring robust global search capabilities.