Specific Parameter-Free Global Optimization to Speed Up Setting and Avoid Factors Interactions
Keywords:Soft computing, optimization, meta-heuristics, parameters complexity, parameters interactions
AbstractMeta-heuristics utilizing numerous parameters are more complicated than meta-heuristics with a couple of parameters for various reasons. In essence, the effort expected to tune the strategy-particular parameters is far more prominent as the quantity of parameters increases and furthermore, complex algorithms are liable for the presence of further parameter interactions. Jaya meta-heuristic does not involve any strategy-specific parameters and is a one-stage technique. It has demonstrated its effectiveness compared to major types of meta-heuristics and it introduces various points of interest, such as its easy deployment and set-up in industrial applications and its low complexity to be studied. In this work, a new meta-heuristic, Enhanced Jaya (EJaya) is proposed to overcome the inconsistency of Jaya in diverse situations, introducing coherent attraction and repulsion movements and restrained intensity for flight. Comparative results of EJaya in a set of benchmark problems including statistical tests show that it is feasible to increase the accuracy, scalability and exploitation capability of Jaya while keeping its specific parameter-free feature. EJaya is especially suitable for a priori undefined characteristics optimization functions or applications where the set-up time of the optimization process is critical and parameters tuning and interactions must be avoided.
Download data is not yet available.
How to Cite
Rodríguez-Reche, R., Prado, R. P., García-Galán, S., Muñoz-Expósito, J. E., & Ruiz-Reyes, N. (2019). Specific Parameter-Free Global Optimization to Speed Up Setting and Avoid Factors Interactions. COMPUTING AND INFORMATICS, 38(2), 265–290. Retrieved from https://www.cai.sk/ojs/index.php/cai/article/view/2019_2_265