Application of Metaheuristic Optimisation Methods to the Design of Guided‐Mode Resonance Filters: A Comparative Study

ABSTRACT The design of optical elements often requires precise optimisation where metaheuristic algorithms have emerged as a powerful approach. This study aims to optimise a guided‐mode resonance reflectance filter chosen due to its extreme parameter sensitivity, which makes it an exemplary platform for evaluating metaheuristic algorithms in photonics. We rigorously compare six established metaheuristic techniques (particle swarm optimisation (PSO), genetic algorithm (GA), shuffled frog leaping algorithm (SFLA), artificial bee colony (ABC), imperialist competitive algorithm (ICA), and differential evolution (DE)) using critical performance metrics. This systematic analysis not only benchmarks algorithm effectiveness for guided‐mode resonance filters but also establishes guidelines for optimising parameter‐sensitive photonic devices.