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This study introduces a novel Chaos Fusion Mutation-Based Weighted Mean of Vectors Algorithm for optimizing unequally spaced linear arrays. The new method achieves superior sidelobe level reduction and deep null steering compared to existing techniques.

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linear antenna arraysmetaheuristic optimizationnull steeringpattern synthesissidelobe suppression

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Area of Science:

  • Optimization Algorithms
  • Antenna Array Synthesis
  • Signal Processing

Background:

  • Optimizing antenna array synthesis is crucial for advanced applications.
  • Existing methods often struggle with complex constraints and achieving deep nulls.
  • The weighted mean of vectors (INFO) framework offers a promising basis for array synthesis.

Purpose of the Study:

  • To propose an advanced optimization algorithm for synthesizing unequally spaced linear arrays.
  • To enhance the weighted mean of vectors (INFO) framework with novel mechanisms.
  • To address the constrained problem of minimizing sidelobe level (SLL) and achieving deep null steering simultaneously.

Main Methods:

  • Developed the Chaos Fusion Mutation-Based Weighted Mean of Vectors Algorithm.
  • Incorporated good-point-set initialization for improved population coverage.
  • Utilized sine-tent-cosine (STC) chaos-based adaptive parameterization for exploration-exploitation balance.
  • Implemented normal-cloud mutation to maintain diversity and prevent premature convergence.
  • Formulated array-factor (AF) optimization as a constrained problem with penalty functions.

Main Results:

  • The proposed algorithm consistently achieved lower peak SLLs and more accurate nulls across diverse array-synthesis tasks.
  • Demonstrated faster and more stable convergence compared to benchmark metaheuristics.
  • Showcased robust superiority over an enhanced IWO in deep-null suppression and SLL reduction.
  • Achieved an SLL of approximately -32.30 dB and a deep null of -125.1 dB at 104° in an engineering example.
  • Confirmed robust convergence and competitive statistical ranking on CEC2020 real-world constrained problems.

Conclusions:

  • The Chaos Fusion Mutation-Based Weighted Mean of Vectors Algorithm is a highly effective technique for unequally spaced linear array synthesis.
  • The integration of chaos-based mechanisms and cloud mutation significantly enhances optimization performance.
  • The algorithm offers a robust and superior solution for complex array synthesis problems with deep null requirements.