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

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.