How to estimate suitability of a particular metaheuristic algorithm?

Estimating the suitability of a metaheuristic algorithm depends on several factors such as the problem at hand, the characteristics of the algorithm, the available computational resources, and the desired solution quality. Here are some general steps to help you estimate the suitability of a metaheuristic algorithm for a given problem:

Understand the problem: Before selecting a metaheuristic algorithm, you need to understand the problem you are trying to solve, including its size, complexity, constraints, and objectives. Different metaheuristic algorithms have different strengths and weaknesses, and some may be better suited for certain types of problems than others.

Review the literature: Conduct a literature review to identify existing research that has applied metaheuristic algorithms to similar problems. This will help you gain insights into which algorithms have been successful and what modifications were made to the algorithms to improve their performance.

Evaluate algorithm characteristics: Evaluate the characteristics of the metaheuristic algorithm, including its ability to handle constraints, convergence speed, scalability, robustness, and flexibility. These characteristics should align with the requirements of your problem.

Analyze computational resources: Determine the computational resources that are required to run the algorithm. Some metaheuristic algorithms require significant computational resources, while others are more computationally efficient. Consider the available hardware and the amount of time and memory that can be allocated to the algorithm.

Perform experiments: Finally, perform experiments to evaluate the performance of the algorithm on the problem at hand. You can use benchmark problems or real-world data to evaluate the algorithm's performance. Compare the results obtained using the metaheuristic algorithm with those obtained using other optimization techniques to determine the algorithm's suitability.

In summary, selecting a metaheuristic algorithm that is suitable for a given problem requires a thorough understanding of the problem, the characteristics of the algorithm, available computational resources, and experimentation to evaluate the algorithm's performance.

"You can use the Rao algorithm. I experimented with Rao-1 optimization, and most of the time, it successfully attains the optimal solution."

You can find

https://sites.google.com/view/raoalgorithms/

https://growingscience.com/ijiec/Vol11/IJIEC_2019_16.pdf

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