Comparison of Metaheuristic Methods for Solving Constrained Problems

Optimization algorithms and metaheuristic algorithms are effective techniques for solving unconstrained and constrained engineering problems. Metaheuristic algorithms are iterative search processes that efficiently explore the solution space. These methods effectively and efficiently search for solutions that are close to the optimal solution. The purpose of this research is to examine the efficiency of three algorithms: Flower Pollination Algorithm (FPA), Elevator Kinematic Optimization (EKO), and Rider Optimization Algorithm (ROA) in solving unconstrained and constrained problems. To compare the efficiency of these algorithms, we use the average, standard deviation, processing time, and signal-to-noise ratio. The experimental results demonstrate that ROA significantly outperforms the other two metaheuristic methods in terms of precision and quality of results, as well as requiring fewer searches to achieve the best solution. In this paper, the ROA can find better optimal solutions, but it gives higher standard deviation values. Additionally, ROA is easier to apply and does not require parameter tuning, while the FPA and EKO methods need parameter tuning for optimal performance.