| Summary: | 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.
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