Hybrid Layered Encoding Cascade Optimization Model in Two-Stage Amplifier Circuit Design

• Published in: 2024 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2024
• Main Author: Fadzal N.; Fadzal A.N.; Kamarulzaman N.H.; Omar S.; Ahmad N.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201314576&doi=10.1109%2fISIEA61920.2024.10607197&partnerID=40&md5=64c15fb08732b84015fff42d041de4ac

The integration of hybrid computational intelligence techniques into the Layered Encoding Cascade Optimization (LECO) model is driven by a balanced approach, combining exploration through Genetic Algorithm (GA) for broader solutions and focused local search employing Particle Swarm Optimization (PSO). In this model, a distinct external layer introduces a layer encoding structure with multi-resolution capabilities. This structure enables GA functions for integral value individuals, while conducting PSO functions for real-valued individuals as potential solutions. To estimate the efficacy of the GA-PSO LECO model, a specific study involving the design of a two-stage amplifier circuit is carried out. Results indicate that the GA-PSO LECO model surpasses existing APLAC built-in optimization methods, achieving a minimum on S (1,1) and S (2,2) at the same time, simultaneously maximizing dB [S (2,1)] to 16.469 dB from the two-stage amplifier circuit. © 2024 IEEE.