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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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A modified PSO structure resulting in high exploration ability with convergence guaranteed.

Xin Chen1, Yangmin Li

  • 1Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macao SAR, China.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 12, 2007
PubMed
Summary

Particle swarm optimization (PSO) is enhanced with controllable random exploration velocity (PSO-CREV) to improve exploration and convergence. This modified algorithm balances exploration and convergence rates for diverse optimization problems.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique inspired by social behavior.
  • Traditional PSO may face challenges in balancing exploration and convergence for complex problems.

Purpose of the Study:

  • Introduce a modified PSO algorithm, PSO with controllable random exploration velocity (PSO-CREV).
  • Enhance the exploration capability of PSO while maintaining convergence efficiency.
  • Analyze the theoretical convergence properties of the proposed algorithm.

Main Methods:

  • Modified the velocity updating principle of PSO by introducing a decreasing coefficient.
  • Incorporated a random velocity component to the velocity updating mechanism.
  • Proved the convergence of PSO-CREV using Lyapunov theory for stochastic processes.

Main Results:

  • Demonstrated that PSO-CREV exhibits properties like 'divergence before convergence'.
  • The added stochastic components provide 'controllable exploration' capabilities.
  • Validated the feasibility and performance of PSO-CREV through benchmark testing.

Conclusions:

  • PSO-CREV offers an improved approach to optimization by balancing exploration and convergence.
  • The theoretical analysis confirms the algorithm's convergence properties.
  • PSO-CREV shows promise for effectively solving a variety of optimization problems.