Relatively Weighted Three Factor Novel Fitness Function for Multi-Objective Optimization of Mspa Using Genetic Algorithm

Аннотация

Micro Strip Patch Antenna (MPSA) is designed using standard design equations and multiple antenna parameters; mainly patch dimensions as variables. The theoretical values may not result in the desired performance. For over a quarter century, evolutionary algorithms have played a pivotal role in optimizing the design variables of MSPA, Genetic Algorithm (GA) being the most popular among them. The success of using GA for the optimization of MSPA lies in the right formation of fitness function (FF) using the right performance parameters. This study introduces a relatively weighted, three-factor novel FF for multi-objective performance optimization of MSPA by optimizing multiple antenna design parameters using GA. The proposed FF guided GA to simultaneously achieve multiple performance optimization objectives: resonance frequency fr of 5 GHz, bandwidth (BW) exceeding 300 MHz, and return loss S11 better than −50 dB by optimizing four critical design parameters—antenna width, length, and slot positions along the x and y axes. The performance of the proposed FF is then compared with six existing FFs from prior research, each with limited optimization capabilities. The results demonstrate that the proposed FF significantly improves optimization outcomes, yielding an MSPA that resonates exactly at 5 GHz, achieves a bandwidth of 315 MHz, and enhances return loss to −54.84 dB. These results confirm the effectiveness of the proposed FF in achieving high-performance, multi-objective, multi-parameter optimization of MSPAs using GA