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

Wenjie Tang1, Li Cao1, Yaodan Chen1

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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.

Keywords:
Levy flightdandelion algorithmfunction optimizationmulti-objective optimizationparticle swarm optimization algorithm

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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).
  • 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.