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Constrained Planar Array Thinning Based on Discrete Particle Swarm Optimization with Hybrid Search Strategies.

Wanhan Cai1, Lixia Ji1, Chenglin Guo1

  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.

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|October 14, 2022
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Summary

A new Discrete Particle Swarm Optimization (DPSO) algorithm enhances large array thinning by integrating global and local search strategies. This method improves population diversity and optimizes particle movement for better performance.

Keywords:
array thinningparticle swarm optimizationpeak side-lobe levelsearch strategy

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Area of Science:

  • Engineering
  • Computer Science
  • Optimization Algorithms

Background:

  • Large array thinning is crucial for efficient system design.
  • Existing optimization algorithms may lack diversity and convergence speed.
  • Discrete Particle Swarm Optimization (DPSO) offers a potential framework for addressing these challenges.

Purpose of the Study:

  • To introduce a novel, enhanced Discrete Particle Swarm Optimization (DPSO) algorithm for large array thinning.
  • To improve population diversity and convergence efficiency in optimization processes.
  • To validate the algorithm's effectiveness in reducing peak side-lobe levels (PSLL).

Main Methods:

  • Integration of global learning strategies for early-stage population diversity.
  • Incorporation of dispersive solution sets and gravitational search algorithm for particle velocity updating.
  • Application of a local search strategy and adaptive mutation probability for late-stage optimization.

Main Results:

  • The enhanced DPSO algorithm demonstrates improved population diversity.
  • Effective particle velocity updating mechanisms contribute to robust optimization.
  • Adaptive particle position adjustment and motion monitoring enhance performance.
  • Verified reduction in peak side-lobe level (PSLL) performance.

Conclusions:

  • The proposed enhanced DPSO algorithm offers a robust and effective solution for large array thinning.
  • The integration of diverse search strategies significantly improves optimization outcomes.
  • The algorithm's performance is validated through representative examples, showing reduced PSLL.