Metaheuristic Algorithms for Engineering and Combinatorial‎Optimization: A Comparative Study Across Problems Categories and Benchmarks

View Full Paper

Ask AI

Bookmark

No single algorithm universally outperforms others across all problem types, indicating variability in algorithm effectiveness.
Ant Colony Optimization excels particularly in discrete problems like the Traveling Salesman Problem and Knapsack Problem.
Evaluation involved performance benchmarking on five distinct optimization problems to assess convergence speed and solution quality.
Future research may need to focus on dynamic parameter tuning and hybrid models to enhance algorithm performance in real-world applications.

Discussion

No takes yet. Share an insight, caveat, or question.

This analysis evaluates metaheuristic algorithms across problem categories, highlighting their performance in optimization tasks.

Shaban et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark