Improved Hybrid Real-Binary PSO Using Modified Learning and Restarting Methods for Planar Microwave Absorber Designs

Abstract

This paper proposes an improved hybrid real-binary particle swarm optimization (IHPSO) using modified learning and restarting methods to address electromagnetic optimization problems involving both continuous and discrete decision variables. The modified learning approach aims to improve the exploration and exploitation capabilities of IHPSO. A restarting mechanism is employed to randomly reposition the swarm of particles to prevent them from being stuck in the local optimum, while still directing the search process with the global best. To demonstrate the performance of the IHPSO, 23 benchmark functions are tested, and the results are compared with those of other traditional algorithms. In both unimodal and multimodal functions, IHPSO outperforms the original hybrid real-binary PSO. Finally, the proposed algorithm is used to design planar microwave absorbers, a classic hybrid real-binary electromagnetic optimization problem.