An Automated Dynamic Solution Space Adaptation in Particle Swarm Optimization

This paper presents a novel adaptive solution space methodology integrated with Particle Swarm Optimization (PSO) to dynamically adjust search boundaries in response to swarm movement within the search space. Traditional PSO can be limited when the global optimum lies outside the initially defined search space, leading to extended search times without reaching target fitness values. The proposed adaptive approach overcomes this limitation by automatically expanding the solution space in relevant dimensions, promoting efficient convergence toward the global optimum while minimizing manual intervention. The performance of the proposed method is assessed through tests on mathematical benchmark functions and three different antenna designs: a dual-band microstrip patch antenna, a six-element Yagi-Uda antenna, and a six-element series-fed microstrip patch antenna. The dual-band and series-fed antennas were simulated in Ansys HFSS using a custom Python-HFSS interface, while the six-element Yagi-Uda antenna was optimized using MATLAB. Results indicate that the adaptive solution space significantly enhances PSO’s effectiveness, achieving optimal design configurations across diverse electromagnetic applications, demonstrating reduced computational costs and improved optimization accuracy. The methodology described in this paper could also be applied to other nature-inspired optimization techniques.