The variable mutant population size strategy in BRKGA-VM may have some potential drawbacks or limitations:
Convergence Issues: A high variability in the mutant population size could lead to premature convergence or stagnation in certain regions of the solution space. This may hinder the algorithm's ability to explore diverse solutions.
Parameter Sensitivity: The performance of the algorithm could be sensitive to the initial settings of the mutant percentage parameters. Suboptimal parameter choices may impact the algorithm's effectiveness.
Computational Overhead: Adapting the mutant population size dynamically adds computational complexity to the algorithm. This could result in increased runtime and resource requirements.
Limited Generalization: The effectiveness of the variable mutant population strategy may vary across different problem instances. It may not always provide significant improvements in all scenarios.
These limitations can be addressed by:
Fine-Tuning Parameters: Conducting thorough parameter tuning and sensitivity analysis to identify optimal settings for the mutant population size parameters.
Hybridization: Integrating the variable mutant strategy with other optimization techniques or metaheuristics to enhance performance and address convergence issues.
Adaptive Mechanisms: Implementing adaptive mechanisms that dynamically adjust the mutant population size based on the algorithm's progress or problem characteristics.
Experimental Validation: Conducting extensive experimental validation on a diverse set of problem instances to assess the robustness and generalization capabilities of the approach.