This paper introduces a novel hybrid optimization algorithm, Adaptive Hybrid PSO-Embedded GA (AHPEGA), which dynamically adapts to optimization performance by integrating Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). The primary objective is to enhance the neuroevolutionary training of multilayer perceptron-based controllers (MLPCs) through the joint optimization of model parameters and structural hyperparameters. Traditional training methods frequently encounter issues such as premature convergence and limited generalization. AHPEGA addresses these limitations through an adaptive training strategy that dynamically adjusts parameters during the evolutionary process, thereby improving convergence speed and solution quality. By effectively reducing entrapment in local minima and balancing exploration and exploitation, AHPEGA improves the quality of neural controller design. The algorithm's performance is evaluated against conventional optimization methods, demonstrating significant improvements in accuracy, convergence speed, and consistency across multiple runs. The practical applicability of the proposed method is demonstrated through simulation in the context of a VSC-based islanded microgrid (MG), where ensuring reliable and effective control under variable operating conditions is critical. This highlights AHPEGA's capability to optimize intelligent control strategies in MG systems, particularly under dynamic and uncertain conditions, reinforcing its practical value in real-world energy environments.