Optimization is a widely used concept in many fields, such as engineering, economics, management, physical sciences, and social sciences.Its purpose is to identify the global maximum or minimum of a fitness function.Finding all optimal points of an objective function can aid in selecting a robust design that simultaneously considers various constraints and performance criteria.Designers of microwave and antenna systems face the challenge of finding optimal solutions for electromagnetic problems of increasing complexity.This can be a difficult task as it involves evaluating electromagnetic fields in three dimensions, considering a large number of parameters and complex constraints, and dealing with nondifferentiable and discontinuous regions.These optimization problems are often non-linear and more challenging to solve than linear ones, especially when many locally optimal solutions are in the feasible region.When developing electromagnetic systems, it is essential to carefully consider how the different design elements interact with each other.Instead of relying on brute-force computational techniques, experts use advanced optimization procedures to achieve the best results.These procedures can be grouped into two categories:Electromagnetic design problems involve optimizing multiple parameters that are nonlinearly related to objective functions.Traditional optimization techniques require significant computational resources that grow exponentially as the problem size increases.Therefore, a method that can produce good results with moderate memory and computational resources is desirable.Bioinspired optimization methods, such as particle swarm optimization (PSO), are known for their computational efficiency and are commonly used in various scientific and technological fields.In this article we explore the potential of advanced PSO-based algorithms to tackle challenging electromagnetic design and analysis problems faced in real-life applications.It provides a detailed comparison between conventional PSO and its quantum-inspired version regarding accuracy and computational costs.Additionally, theoretical insights on convergence issues and sensitivity analysis on parameters influencing the stochastic process are reported.The utilization of a novel quantum PSO-based algorithm in advanced scenarios, such as reconfigurable and shaped lens antenna synthesis, is illustrated.The hybrid modeling approach, based on the unified geometrical description enabled by the Gielis Transformation, is applied in combination with a suitable quantum PSO-based algorithm, along with a geometrical tube tracing and physical optics technique for solving the inverse problem aimed at identifying the geometrical parameters that yield optimal antenna performance.
DOI: https://doi.org/10.55060/s.atmps.231115.010
Publish Year: 2023
