<p>Optimizing systems with an unknown number of critical homogenous components presents a significant challenge in many real-world applications. Traditional metaheuristic approaches often require predefined component counts, leading to suboptimal solutions when this number is uncertain. To address this, we propose a chaotic map-encoded metaheuristic framework that dynamically adjusts the number of components during optimization. This approach is applied to two complex problems: dendritic neuron model (DNM) optimization, where the goal is to determine the optimal number of dendritic branches for improved learning performance, and wind farm layout optimization (WFLOP), which seeks to optimize the placement and number of wind turbines to maximize energy output while minimizing wake effects. Experimental results show that this approach outperforms variable-length genetic algorithms in DNM optimization and demonstrates competitive performance in WFLOP. These findings highlight the potential of chaotic maps to improve metaheuristic efficiency in variable-length optimization problems.</p>