A hybrid swarm intelligent optimization algorithm for antenna design problems.

Area of Science:

• Computational intelligence
• Optimization algorithms
• Electromagnetics

Background:

• Meta-heuristic algorithms are vital for complex problem-solving but often face limitations like local optima.
• The Naked Mole-Rat Algorithm (NMRA) shows potential but requires improvements in convergence and accuracy.
• Existing algorithms struggle with diverse optimization challenges, necessitating novel approaches.

Purpose of the Study:

• To introduce an enhanced Naked Mole-Rat Algorithm (NMRA) named Salp Swarm and Seagull Optimization-based NMRA (SSNMRA).
• To improve the exploration capabilities and convergence performance of the original NMRA.
• To validate the efficacy of SSNMRA on benchmark test suites and electromagnetic optimization tasks.

Main Methods:

• Hybridization of the Naked Mole-Rat Algorithm (NMRA) with the Salp Swarm Algorithm (SSA) and Seagull Optimization Algorithm (SOA).
• Integration of SSA and SOA search mechanisms to enhance NMRA's exploration and exploitation balance.
• Validation using the CEC 2019 benchmark test suite and practical electromagnetic optimization problems.

Main Results:

• SSNMRA demonstrated superior performance compared to existing state-of-the-art algorithms.
• The proposed hybrid algorithm achieved enhanced convergence accuracy.
• Significant improvements in overall optimization capability were observed.

Conclusions:

• SSNMRA offers a promising solution for complex optimization problems, particularly in electromagnetics.
• The integration of SSA and SOA effectively addresses NMRA's limitations.
• The algorithm shows potential for advanced applications such as complex antenna design.