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  11. Solving Engineering Optimization Problems Based On Multi-strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm.

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm.

Wenjie Tang1, Li Cao1, Yaodan Chen1

  • 1School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China.

Biomimetics (Basel, Switzerland)
|May 24, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

The new particle swarm optimization hybrid dandelion optimization (PSODO) algorithm enhances swarm intelligence methods. It improves optimization speed and avoids local extremum issues for better problem-solving.

Area of Science:

  • Computational intelligence
  • Swarm intelligence optimization

Background:

  • Swarm intelligence methods are widely used in mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization.
  • The dandelion optimization algorithm faces challenges with slow optimization speed and susceptibility to local extremum.

Purpose of the Study:

  • To propose a hybrid algorithm, particle swarm optimization hybrid dandelion optimization (PSODO), addressing the limitations of the standard dandelion optimization algorithm.
  • To enhance the diversity and search capabilities of optimization algorithms.

Main Methods:

  • A hybrid approach combining particle swarm optimization (PSO) with the dandelion optimization algorithm.
  • Incorporating PSO's global search capabilities and the dandelion algorithm's unique individual update rules (rising, falling, landing).
Keywords:
Levy flightdandelion algorithmfunction optimizationmulti-objective optimization

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  • Balancing global and local search through the dandelion's ascending and descending stages.

    • Main Results:

    • The PSODO algorithm demonstrates significantly improved global optimal value search ability.
    • Enhanced convergence speed and overall optimization speed compared to other algorithms.
    • Effectiveness verified on 22 benchmark functions and three engineering design problems (CEC 2005).

    Conclusions:

    • The PSODO algorithm effectively overcomes the limitations of the original dandelion optimization algorithm.
    • The hybrid approach offers a superior balance between global exploration and local exploitation.
    • PSODO presents a viable and effective optimization technique for complex problems.
    particle swarm optimization algorithm