Advances in Parameter-Tuning Techniques for Metaheuristic Algorithms

Dear Colleagues,

Metaheuristic algorithms are advanced computational strategies considered state-of-the-art solvers often used in complex computational optimization problems where traditional methods are inefficient or infeasible. These algorithms are particularly beneficial due to their flexibility, robustness, and ability to provide sub-optimal solutions even in complex and non-linear search spaces in a reasonable time. However, their performance is strongly correlated with proper parameterization. Optimal tuning is a crucial aspect and can have a significant performance impact on their applications as it controls the balance between exploration (global search) and exploitation (local search) in the search space. Fine-tuning can result in a local optimal or inefficient search. Therefore, ensuring proper tuning/control is a fundamental step in their applications to the corresponding problem in order to deliver efficient, accurate, and reliable solutions.

This Special Issue is devoted to state-of-the-art research in parameter-tuning techniques for metaheuristic algorithms and their application to various problems. Thus, the Guest Editors would like to provide an opportunity to present recent developments in the areas mentioned above, and the topics of this Special Issue include, but are not limited to, the following:

Parameter tuning, parameter control, offline parameter tuning, online parameter tuning, computational optimization, metaheuristic algorithms, swarm intelligence, population-based, genetic algorithms, and applications in real-world problems or benchmark functions.

This Special Issue welcomes research exploring further topics in addition to the abovementioned. 

We hope this initiative will attract researchers specializing in the above areas of interest and encourage them to submit their novel research to this Special Issue.

Dr. Vasileios A. Tatsis
Guest Editor

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• computational optimization
• metaheuristic algorithms
• parameter tuning
• swarm intelligence
• real-world applications